6.2 Removing Color Casts
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Next: 6.3 Repairing Blemishes with Up: 6. Touchup and Enhancement Previous: 6.1 Improving Tonal Range
Subsections

- 6.2.1 The `Curves` Tool
- 6.2.2 Color Correcting by Balancing the Neutrals
- 6.2.3 Finding the Shadow, Midtone, and Highlight
- 6.2.4 Other Colors We Know
- 6.2.5 The Perturbation Technique
- 6.2.6 Getting More Detail into the Subject
- 6.2.7 Other Color Correcting Tools
  - 6.2.7.1 The `Color Balance` Tool
  - 6.2.7.2 The `Brightness-Contrast` Tool
  - 6.2.7.3 The `Hue-Saturation` Tool

#
6.2 Removing Color Casts

A color cast occurs when the Red, Green, and Blue channels of an image are not properly balanced. The cast can be across the entire range of pixel values or can limit itself to the highlight, shadow, or midtones of the image. Color casts are common in photographs. They occur because, under certain circumstances, the sensitivity of film to color is different than the sensitivity of the human eye. The film just doesn't record the same scene as your eye.

Removing color casts requires being able to identify where the color in an image has gone wrong. I often have a hard time telling, simply by looking at an image, if it is suffering from a color cast. This is due to many reasons. First, color is a perceptual issue that is strongly affected by surrounding light conditions. Second, the representation of color varies from one computer monitor to another because settings such as brightness, contrast, gamma, and color temperature can be quite different. Furthermore, the ability of a monitor's phosphors to create levels of red, green, and blue light will differ from monitor to monitor and change over time as the monitor ages.

All this makes the perceptual evaluation of color casts difficult for the average person. Fortunately, the GIMP provides some analytical and interactive tools for determining whether color casts exist and for correcting them. There are several techniques for identifying, measuring, and correcting color casts. Each approach uses the powerful `Curves` tool which is reviewed in the first part of this section.

##
6.2.1 The `Curves` Tool

The `Curves` tool is found in the `Image:Image/Colors` menu. Figure 6.8

Figure 6.8: The `Curves` Tool

illustrates the basic components of this tool. As with the `Levels` tool, the `Curves` tool can be applied to any of five different channels; however, for removing color casts, we are mainly concerned with the Red, Green, and Blue channels.

The main elements of the tool consist of an input value domain, an output value range, and a control curve drawn on a graph. The graph has a grid divided into quarters, and the range of values is from 0 to 255. Thus, the value of each grid line, moving horizontally from left to right, is 0, 64, 128, 192, and 255. The values moving vertically from bottom to top are the same.

The control curve represents a map of the input value domain to the output value range. That sounds pretty abstract! What good is mapping input to output values? The executive, top-level answer is that curve mapping of input to output values is the most powerful color correcting tool in the GIMP, and learning how to use it is definitely worth your while. More about mapping input to output values and what it's good for in a moment...

Figure 6.9

Figure 6.9: Adding and Moving a Control Point

illustrates the main operation we will be performing with the `Curves` tool--that is, adding a control point to the control curve as in Figure 6.9(a) and then moving it to a new position as in Figure 6.9(b). Control points can be added to the curve simply by clicking on the curve at the desired location. The point can be moved by clicking and dragging it. If the mouse cursor is not on the curve when the mouse button is clicked, a control point is added to the curve at the position directly above (or below) the cursor. This new point is then automatically positioned to the cursor location.

Every control curve has two default control points located at the curve's upper right and lower left. These can be moved just like user-created control points. All control points except the defaults can be removed by clicking on the Reset button in the dialog. If many control points have been positioned and a single one needs to be removed you can remove it by dragging it with the mouse to the left or right edge of the dialog. This pulls the control point right off the curve.

The `Curves` tool also has an Information Field that interactively indicates the X and Y positions of the mouse cursor whenever it is in the graph area of the dialog. This field is located in the upper-left corner of the graph area, and, as you will see, this information is essential for performing precise color correction. But first, it is important to get an intuitive feel for how the `Curves` tool works.

Figure 6.10

Figure 6.10: Image with Shadow-to-Midtone and Midtone-to-Highlight Gradients

illustrates a special test case that will help you understand how the `Curves` tool affects an image. The figure shows two gradients each using up half the tonal range of a grayscale. The upper gradient has values from 0 to 127, and the lower gradient has values from 128 to 255.

The following illustrates how the `Curves` tool is used to change the tonal range of these two gradients. Figure 6.11(a)

Figure 6.11: Improving the Contrast of the Midtone-to-Highlight Gradient

shows the Value channel of the `Curves` tool. A control point has been placed at the midpoint of the curve and pulled downward to a position one quarter of its original height. Initially, when it is a straight line, the curve of the `Curves` tool maps each input value to the identical output value. However, the curve shown in Figure 6.11 changes that map. Now the input values from 0 to 128 are mapped to one quarter of the scale they were before. That is, these values are now compressed into the range of 0 to 32. At the same time, the input values in the domain 128 to 255 are stretched to the range of 32 to 255. This is emphasized by the red dashed lines superimposed on the `Curves` dialog.

This means that all the pixels that had values in 0 to 128 in Figure 6.10 are compressed to a new range of 0 to 32. Thus, much of the detail and contrast between neighboring pixel values in this range is lost. This effect can clearly be seen in the upper gradient in Figure 6.11(b), which is the result of applying the curve in Figure 6.11(a) to the image in Figure 6.10. Simultaneously, the pixel values in the domain 128 to 255 are stretched to a new range of 32 to 255. Consequently, the contrast of the detail of these pixels is increased, as you can see in the lower gradient in Figure 6.11(b).

A similar analysis can be made for Figure 6.12,

Figure 6.12: Improving the Contrast of the Shadow-to-Midtone Gradient

which illustrates the opposite effect. Here, as shown in Figure 6.12(a), the input value domain from 0 to 128 is stretched and the input domain from 128 to 255 is compressed. The effect on the gradients is shown in Figure 6.12(b). Although these two examples are illustrated using the Value channel, the same conclusions hold for the Red, Green, and Blue channels.

The conclusion that can be drawn from these two examples is that the `Curves` tool can be used for two things. First, ranges of pixel values can be remapped. This is particularly valuable when a color channel is out of balance with the others. When color imbalances occur, we will try to measure which range is out of balance, determine what the range should be, and use the `Curves` tool to remap one range to the other. This approach is developed in detail in Section 6.2.2.

The second use is to improve contrast where it is most needed. Often an image has a subject that is much more important than the rest of the image. When this is the case, it is desirable to give the subject the most detail and contrast possible. From the examples, you can see that the `Curves` tool can be used to improve contrast. Note, however, that in improving the contrast in one range of values, there must simultaneously be a loss of detail and contrast in the complementary range of values. This was clearly demonstrated in Figures 6.11(b) and 6.12(b). Fortunately, improving the contrast of the subject while simultaneously impoverishing the contrast of the background is typically what we want to do. This draws the viewer's eye to the part of the image we most want to convey. The idea of improving subject contrast is developed more in Section 6.2.6.

##
6.2.2 Color Correcting by Balancing the Neutrals

A powerful method for identifying color casts is to measure the color of pixels that, in principle, should be neutral gray. Neutrals must have equal components of red, green, and blue. If they don't, that indicates the presence of a color cast, and you know that a color correction must be made.

Figure 6.13

Figure 6.13: Image with a Color Cast? The Measured Pixels Say Yes!

illustrates a case where the identification of neutral pixels allows us to color correct the image. In the image, the arcade of palm trees casts shadows over a white sand path and, in principle these shadows should be neutral in color.

There are a wide range of values for the shadow along the path, and it is possible to measure dark, mid-range, and light shadow values. In Figure 6.13, these are referred to as shadow, midtone, and highlight neutrals, and their values, measured with the `Color Picker`, are shown at three different points. (The `Color Picker` is located in the Toolbox and is represented by the eye-dropper icon.) Measuring color in an image with the `Color Picker` displays the color in a rectangular patch in the `Color Picker` dialog and gives its R, G, and B values. Measuring a color with the `Color Picker` also has the effect of setting the `Active Foreground Color` patch, in the Toolbox window, to the measured color.

For the three measured pixels shown in Figure 6.13, you can see that there is a distinct blue tinge for each one. Not only do the color patches shown in the `Color Picker` dialog look blue, the measured R, G, and B values show that there is a significant deviation of the blue values from the red and green ones--too much for the color of these pixels to be neutral. Using the color notation introduced in Section 5.1, the color patches shown in the `Color Picker` dialogs have the values 33R 35G 52B for the shadow, 111R 132G 179B for the midtone, and 173R 172G 206B for the highlight. The measured R, G, and B values for each point clearly indicate that there is too much blue.

It is true that shadows sometimes appear blue. However, this is usually true in winter away from the equator and is due to the natural filtering of the sun's rays by the earth's atmosphere. The blue color cast measured in Figure 6.13 is more likely due to the tendency of film, especially slide film, to produce blue casts when photographing under natural sky light. In any case, at an equatorial location on the earth, we would expect the fuller spectrum of the sun's light to create neutral shadows when these are seen on a neutral background.

In addition to the blue cast, there may also be a slight red deficiency in the midtone range. In the following discussion the blue color cast and the red midtone deficiency are corrected using the `Curves` tool.

Figures 6.14

Figure 6.14: Using the Measured Pixel Values to Modify the Red Curve

and 6.15

Figure 6.15: Using the Measured Pixel Values to Modify the Blue Curve

illustrate how the `Curves` tool is used to correct these color problems. Figure 6.14 shows a modification to the red curve and Figure 6.15 to the blue. For each, the procedure is identical. The red and blue components of the measured pixel values are used to place control points on their respective curves. This is shown in part (a) of each figure. The accurate placement of the points is facilitated by the Information Field found in the upper left corner of the graph area. The control points are then moved vertically up or down to new positions, which remaps the ranges of pixel values between them and makes the measured pixels neutral in value. The displaced control points are shown in part (b) of the figures.

Again, the goal in displacing the control points is to make each of the measured pixel values neutral. This means making their red, green, and blue components equal. In this example, this is accomplished by moving the red and blue control points so that their values are made equal to the measured green values. The accurate repositioning of the points is made possible by the position Information Field displayed in the upper-left corner of the graph area. Note that the numbers positioned near the control points in Figures 6.14 and 6.15 are not a feature of the `Curves` tool; they are placed there to clarify the procedure.

Thus, on the red control curve, the shadow, midtone, and highlight values are moved from 33, 111, and 173 to the measured green values of 35, 132, and 172. On the blue control curve, the shadow, midtone, and highlight values are moved from 52, 179, and 206, again, to the green values of 35, 132, and 172. After the operation, the color values for the three measured pixels are 35R 35G 35B for the shadow, 132R 132G 132B for the midtone, and 172R 172G 172B for the highlight--all three neutral grays.

The result of color correcting the neutral pixel values is shown in Figure 6.16(b).

Figure 6.16: Comparison of the Original and Color Corrected Images

For comparison, the original image is shown in Figure 6.16(a). It is quite clear now that the original image did have a blue cast and that this has been eliminated in the corrected image. Measuring the pixel values along the tree-lined path shows that, overall, the balance is much better and most of the tree shadow values are now neutral. Furthermore, the rest of the image has taken on a much warmer look. The trees are now bathed in a yellow light, corresponding better to what we might expect from a tropical sunlit scene.

There are some practical questions about the color correcting procedure just described. The first is, why were the blue and red channels matched to the green? For the three measured pixels there are a total of nine ways to make the three neutral. However, in practice, it is typical that two of the channels are almost the same and that one is quite different. When this is the case, as it is for the the measured shadow and highlight values in the preceding example, the choice is clear. When it is not the case, some experimentation may be necessary.

The second question about the procedure is, why measure three points? The method doesn't require three points and, amazingly, often a single point can suffice to color correct the entire image. However, matching a shadow, midtone, and highlight image point provides additional insurance that the color in each range is properly balanced.

The `Curves` tool has several features that facilitate the positioning of points on the control curves. Clicking the mouse button in the image window produces a vertical bar in the graph area of the `Curves` tool. The bar position corresponds to the pixel value the mouse cursor is over in the image window. Clicking and dragging the mouse button interactively updates the position of the vertical bar. In this way, it is possible to see where different pixel values in the image are located on the control curve and helps to discover the locations of shadow, midtone, and highlight pixels. In addition to input position information, Shift-clicking in the image window automatically creates a control point on the curve in the active channel of the `Curves` dialog. Control-clicking on a point in the image window produces control points on each of the Red, Green, Blue, and Value control curves.

In addition to the `Curves` tool features, a very useful tool for exploring and discovering color problems in an image is the `Info Window` dialog. This dialog is found in the `Image:View` menu and can also be invoked by typing `C-S-i` in the image window. The Extended tab of this dialog interactively reports the R, G, and B pixel-color components when the mouse is in the image window. The advantage of the `Info Window` over the `Color Picker` for measuring pixel values in the image window is it remains open while using the `Curves` tool.

There are two lessons to be learned from this section. First, color correction can be very easy. Measuring only a few pixel values across the shadow to highlight range can color correct an entire image in a few minutes time. Second, the color correction obtained in this way not only fixes the individual measured pixels but usually corrects the entire image. Third, the `Curves` tool is the only one which can be used to correct the image based on measured pixel values. For these reasons, the `Curves` tool is the most precise and the most powerful tool for color correction in the GIMP.

##
6.2.3 Finding the Shadow, Midtone, and Highlight

To do color correction using the techniques of the previous section, it is important to be able to identify shadow, midtone, and highlight colors. To be frank, I sometimes have difficulties finding them for some images. When this happens, the `Threshold` tool is useful because it can show where any range of values is hidden in an image. The `Threshold` tool is found in the `Image:Image/Colors` menu. Figure 6.17

Figure 6.17: Using the `Threshold` Tool to Find Shadows and Highlights: (a) Original Photo (b) Darkest Shadows (c) Lightest Highlights

shows how this tool can be used to help locate critical shadow and highlight values.

Figure 6.17(a) illustrates an image and Figures 6.17(b) and (c) show how to use the `Threshold` tool to identify its darkest shadows and lightest highlights. The `Threshold` tool, which was introduced in Section 4.5.3, consists of a dialog that displays the histogram of the image's pixel values. Clicking and dragging the mouse through a range of histogram values has the effect of mapping all the pixel values in the image to either black or white. The image pixel values corresponding to the selected histogram range become white, and the rest become black.

In this way, it is easy to localize the pixel values we are searching for. Starting with an image like the one in Figure 6.17(a), a duplicate of the image is made by typing `C-d` in the image window. Then, the `Threshold` tool is applied to the duplicate, and a range of shadow values is swept out with the mouse, as shown in Figure 6.17(b). The resulting white pixel values in the duplicated image window show the locations of the darkest shadows. Using the duplicated image as a guide, the `Color Picker` can now be used in the original image window to measure pixel values at the appropriate locations. A similar procedure is used to accurately find and measure the lightest highlights of the image using the duplicated image shown in Figure 6.17(c).

Note that this procedure, using the `Threshold` tool to find value ranges in the image, can also be used on the image's individual RGB components. The decomposition is made using the `Decompose` function, found in the `Image:Image/Mode` menu (see Section 4.5.3).

##
6.2.4 Other Colors We Know

If there are identifiable colors other than neutrals in an image, these, too, can be used to perform color correction. Examples are the colors of flags, logos, or certain animals. A Canadian flag whose famous maple leaf emblem were not red would be a good point of reference for color correction.

Another important class of colors that can be found in many images are fleshtones. A medium Caucasian fleshtone has a green around 192, a red that is about 20% more (234), and a blue about 10% less (176). Darker skinned people have skin colors with more blue and less red, and Asians have less blue. Because of the variability of skin tones, relying on them as guides is more uncertain than using neutrals. Nevertheless, for some images this may be the only point of reference available for color correction.

##
6.2.5 The Perturbation Technique

Sometimes it is impossible to positively identify a color in an image but it seems clear that a color cast is present. This means there are no color references that can be used to do color correction. Under these conditions, an alternate approach is needed. The method proposed here is what I call the perturbation technique. It relies on the visual feedback that the preview checkbox in the `Curves` dialog provides. In a nutshell, the method makes incremental perturbations to the shadow, midtone, and highlight regions for each of the red, green, and blue curves. The perturbations that improve the image are kept and those that do not are discarded.

Figure 6.18

Figure 6.18: Implementation of the Perturbation Technique: (a) Placing the Shadow, Midtone, and Highlight Region Control Points (b) Perturbation of the Midtone Control Point

illustrates the idea. In Figure 6.18(a), the Red channel of the `Curves` tool is displayed, and three control points have been added to the curve at the quarter, half, and three-quarter positions. The regions of the curve around these points roughly control the shadow, midtone, and highlight regions of the Red channel. The perturbation technique works by moving the control points up or down and seeing whether the change improves the image. Figure 6.18(b) shows a perturbation of the midtone control point.

Note that, in moving the midtone control point, the only parts of the curve that move are those between the shadow and highlight control points. The rest of the curve is constrained by the two other control points. This is very useful because it allows the `Curves` tool to act on a select part of the image's tonal range.

The perturbation technique is not scientific and relies on the perceptual abilities of the user to see changes that improve or deteriorate an image. Nevertheless, cycling among the nine control points, making only gradual changes to each, can often produce marvelous results. The following example illustrates this approach.

Figure 6.19

Figure 6.19: Image with a Color Cast. Can you see it?

shows an image of a tiger that has a problem (the image, not the tiger). It has a subtle, overall green color cast. The color cast is so subtle, at first, I didn't even recognize it.

The perturbation technique is an approach to color correction that requires experimentation. Thus, the steps are difficult to present in book format. The best that I can do is to show you the results. For this, Figure 6.20

Figure 6.20: The Three Adjusted Curves Using the Perturbation Technique

shows the final curves for the Red, Green, and Blue channels (shown in parts (a), (b), and (c), respectively). The resulting effect on the image is shown in Figure 6.21.

Figure 6.21: The Color Corrected Image

Comparing Figure 6.21 with Figure 6.19 makes the green color cast in the original image readily apparent. Furthermore, you can see that the application of the perturbation technique has simultaneously improved the contrast of the image's subject. The tiger looks significantly enhanced.

There is an important caveat to the perturbation technique. Because this method relies on the visual feedback you get from your monitor, the technique is highly dependent on the monitor's individual characteristics. What looks great on your monitor might not look as great on another. The method described earlier in this chapter that measures pixel values and then makes adjustments accordingly does not depend on the monitor. The earlier method is the preferred approach whenever possible.

##
6.2.6 Getting More Detail into the Subject

Sometimes one part of an image is more important than the rest. We often refer to this part of the image as the image subject. It is typical to want the subject to have as much contrast as possible. This makes the subject stand out and look much more interesting. As was discussed in Section 6.2.1, the `Curves` tool can be used to improve the contrast of certain parts of the tonal range in an image. This can be applied to the subject of the image by determining its lightest highlight and deepest shadow and then steepening the part of the control curves covering this range.

However, if a lot of work has already gone into maximizing tonal range and correcting color, you might be reticent to play with the curves to get additional contrast into the subject. Clearly, manipulating a part of the curves in an attempt to improve contrast can damage the color balance obtained with much hard work.

Fortunately, there is a way to have your cake and eat it too. Up to this point, the red, green, and blue control curves have monopolized our attention. As you have already seen, modifying any of these changes the overall balance of color in the image. However, the `Curves` tool can also be used to modify the image's Value channel. As discussed in Section 5.3, the Value channel has no effect on color; it only affects brightness. This, then, is the perfect channel for improving contrast while preserving the image's color balance.

To illustrate the use of the Value channel to improve contrast, Figure 6.22(a)

Figure 6.22: Original and Color Corrected Images

shows an original image that lacks tonal range and has a terrible yellow color cast. Figure 6.22(b) shows the corrected image, which was obtained simply by applying the Auto Levels button of the `Levels` tool (see Section 6.1.2). Although the image is much improved, it lacks detail in the eagle's white head feathers. It would be nice to improve this part of the eagle to give it more visual depth. This can be done by modifying the Value channel of the `Curves` tool.

To improve the contrast of the head feathers, it is necessary to determine the value range that this part of the image is contained in. The `Threshold` tool is perfect for this (see Section 6.2.3). Figure 6.23

Figure 6.23: Using `Threshold` to Find the Correct Value Range

illustrates the use of `Threshold` for determining an appropriate range of values. Figure 6.23(a) shows the `Threshold` dialog applied to the color corrected image in Figure 6.22(b), and Figure 6.23(b) shows the result of having swept out the range [174,255] in the `Threshold` dialog. This range, which was determined by experimentation, pretty much covers the part of the eagle image where we want to improve the contrast.

The value range determined with the `Threshold` tool is noted, and the tool is then canceled. The next step is to invoke the `Curves` tool and to improve the contrast of the Value channel curve using the range determined using `Threshold`. Figure 6.24

Figure 6.24: Using `Curves` to Improve Contrast in the Subject Value Range

illustrates the procedure. Figure 6.24(a) shows the `Curves` dialog for the Value channel. A control point has been added to the curve at the input value of 174, and this point has been moved to the output value of 140. This has the effect of steepening the Value curve in the range [174,255], which is the range of values where we want to improve contrast. From previous discussion, we know that steepening the Value curve in a range has the effect of improving this range's contrast.

The result of applying the curve shown in Figure 6.24(a) is shown in Figure 6.24(b). Comparing this result to the image in Figure 6.22(b) shows that the contrast of the eagle's head feathers has been significantly improved. As a final, practical note, the amount that the Value curve is steepened is determined experimentally by moving the control points on the curve and evaluating the effect on the image.

## 6.2.7 Other Color Correcting Tools

The GIMP has several other color correcting tools. These all live in the `Image:Colors` menu and their names are `Color Balance`, `Brightness-Contrast`, and `Hue-Saturation`. In this book, these tools are not covered in detail, and for good reason. Although they can be used for touchup and enhancement, these tools are like working with a dull knife, especially when compared to the `Curves` tool, which has the precision of a surgeon's scalpel. Let's see why.

###
6.2.7.1 The `Color Balance` Tool

Figure 6.25(a)

Figure 6.25: Comparing `Color Balance` to the `Curves` Tool

shows the dialog for the `Color Balance` tool. This allows an image to be adjusted in the shadow, midtone, or highlight regions for the red-cyan, green-magenta, or blue-yellow balance. `Curves` does exactly the same thing except with much greater precision. Raising a curve at a point adds more of the color it represents at the expense of the complementary color around that point. Lowering the curve has the opposite effect, that of shifting the balance towards the complementary color.

The `Curves` tool can do anything the `Color Balance` tool can, but better. The reason `Curves` is more powerful is that the `Color Picker` can be used to identify exactly which input values need color balancing. The `Color Picker` is shown in Figure 6.25(b), and it is displaying a value for a measured pixel in an image. This value can be precisely placed and manipulated in the `Curves` tool. Figure 6.25(c) shows how a control point, corresponding to the green component of the measured pixel, has been placed on the Green channel curve. This placement of a control point, corresponding to a measured pixel value, permits the subsequent, precise correction of color balance at this point. By comparison, the `Color Balance` tool only allows for the gross selection of input regions (shadow, midtone, and highlight) and is incapable of performing the precision color corrections described in detail in Section 6.2.2.

The conclusion is that the `Curves` tool is much more precise and versatile than the `Color Balance` tool.

###
6.2.7.2 The `Brightness-Contrast` Tool

Figure 6.26(a)

Figure 6.26: Comparing `Brightness-Contrast` with the `Curves` Tool

displays the dialog for the `Brightness-Contrast` tool. The way this tool functions can be fully simulated using the `Curves` tool. Figure 6.26(b) illustrates the Value channel of the `Curves` tool, showing that the curve has been displaced upward. Moving the value curve up maps the input value domain to an output range that is uniformly brighter. This modification to the value curve exactly simulates the action of the `Brightness-Contrast` tool when the Brightness slider in the dialog is moved to the right. Moving the Brightness slider to the left corresponds to displacing the value curve downward.

Thus, brightening or darkening an image using the Brightness slider has the effect of diminishing tonal range because the result maps the input value domain to a smaller output range. From the discussion in Section 6.1, this is clearly a disadvantage. The `Curves` tool, on the other hand, can be used to increase or decrease brightness without loosing tonal range. To brighten an image place a control point on the Value curve and displace the point upwards. This brightens the image without losing tonal range. To darken the image drag the point downwards.

Figure 6.26(c) also shows the Value channel of the `Curves` tool. Here, the curve has been rotated counter-clockwise around the center of the input-output dialog. This has the effect of increasing contrast in the midtones of the image. However, it simultaneously eliminates detail in the shadow and highlight ranges. This action is exactly what the `Brightness-Contrast` tool does when the Contrast slider of the dialog is moved to the right. Moving the slider to the left corresponds to rotating the curve in the clockwise direction.

The conclusion is that the `Curves` tool performs much better than the `Brightness-Contrast` tool.

###
6.2.7.3 The `Hue-Saturation` Tool

The `Hue-Saturation` dialog is shown in Figure 6.27.

Figure 6.27: The `Hue-Saturation` Tool

This tool is more complex than either the `Color Balance` or `Brightness-Contrast` tools, and its action cannot be simply reproduced using `Curves`. Nevertheless, there is rarely any reason to use this tool for touchup or enhancement. The `Hue-Saturation` tool allows hue, lightness, and saturation to be adjusted for an image. The adjustment can be made for the entire image or for any combination of the image's red, green, blue, cyan, magenta, or yellow components.

The reason `Hue-Saturation` is not useful for color correction is that it is difficult to know how to make adjustments with it to enhance an image. With the `Curves` tool, the measurements made with the `Color Picker` can be used to make precise changes that will result in predictable improvements to an image. By contrast, there is no way to measure what is wrong in an image in a way that can then be used to make precise corrections with the `Hue-Saturation` tool. Furthermore, as has already been pointed out, color problems are rarely uniform over the shadow, midtone, and highlight regions. The `Hue-Saturation` tool has no capability for varying color components in different ranges of the image.

Thus, for color correction, the `Hue-Saturation` tool is of little use. Nevertheless, unlike `Color Balance` and `Brightness-Contrast`, the `Hue-Saturation` tool can be used to do useful and interesting things that would be difficult to do with any other color tool. Figure 6.28

Figure 6.28: Hue-Saturation

illustrates an application of the `Hue-Saturation` tool which doesn't fit into the category of touchup or enhancement but is of considerable stylistic and artistic interest.

Figure 6.28(a) displays a beach scene consisting of vivid colors, and Figure 6.28(b) shows the `Hue-Saturation` dialog where the Saturation slider for the Cyan radio button has been adjusted to -100%. The result, shown in Figure 6.28(c), is to completely desaturate the sky and water, while leaving the color of the beach untouched. Similarly interesting modifications can be made with the Hue and Lightness sliders. Try experimenting!

Next: 6.3 Repairing Blemishes with Up: 6. Touchup and Enhancement Previous: 6.1 Improving Tonal Range
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