Posts Tagged ‘Brightness curve’

Converting your color images to B&W is a lot easier than you might have thought. The method I’ll show you isn’t the only way, but it’s simple, intuitive and it works. I use Picture Window Pro (PWP) to edit all my images and I’ll be using the monochrome transformation dialog (quite a mouthful, eh?) to illustrate my conversion work-flow. I also tested this method with the Gimp and the conversion using the channel-mixer is similar to PWP. As you go through the tutorial, you can enlarge any of the images with a click for easier viewing.

Click to enlarge

OK, here’s my original color slide of a Saigon Street scene. I’ll walk you through all the steps needed to convert this color image to a convincing B&W image, but first, a quick explanation to help you understand how this all works.

You’ll be extracting your B&W image from the color image with a colored filter using the RGB channels. The color you choose as a filter will lighten the values of that color and darken the values of “opposite” colors. I used red and green colors with similar tonal values as an example in Part I. Choose a green filter and the green will be a lighter monochrome tone, red will be darker and we get our contrast back. A more dramatic example would be an image of a red flower extracted using the red channel. The flower will be white in the conversion.

Let’s get started then. Choose Transformation>Color>Monochrome from the main menu in PWP and you get this dialog. Green is the default, but we want to expand our choices.

Click on the green filter icon and this expanded dialog pops up. The readout scale in the lower left shows the amount for each of the RGB channels on a scale of 0 to 100. This particular example is green so G is 100 while R and B are zero. Look to the right and you see a series of colored stripes. Ignore the black, gray and white. The other six stripes are Red, Green, Blue, Cyan, Magenta and Yellow (reading left to right).

One of these six will usually do the job and they are what I use most of the time. Simply click on the color stripe you want. If you like, you can actually use the eyedropper probe on your original color image to pick any color filter you want, but that’s a topic for a more advanced tutorial.

Let’s see what the green filter gives us. Pay attention to the two guys in the right foreground to see the effect of this green filter and the other color filters we’ll try later. Green is too dark and I’m not happy with the contrast.

Here’s another version using the yellow filter instead. It’s lighter than the green, but contrast isn’t where we want it yet.

Here’s the dialog for the cyan filter. I got this simply by clicking on the cyan stripe. Notice the readout. R=0, B=100 and G=100.

I like this one. The entire image is lighter and has more “pop.” Now look at the guy in the striped shirt. See the tonal changes? The trees and buildings in the distance at the end of the street have lightened and enhanced the feeling of depth. The best way to discover the right conversion filter is to experiment until you find the one you like. As I said, you’ll only need one of these six filters most of the time.

This screen-shot is the channel mixer dialog in the Gimp. You can get the exact same results from this dialog as you can from the PWP mono transformation. The mono transform in PWP is more sophisticated and easier to use — just pick a color. With the Gimp, you must set the numbers yourself. Click on Monochrome and preserve luminosity, then enter the numbers. This is cyan, equal mix of blue and green with no red at all so I set Red to zero, Blue to 100 and Green to 100. These numbers are percentages so the total is supposed to add up to 100. I tried using 50 for Blue and 50 for Green and saw no difference.

You can’t argue with the cost of the Gimp, it’s open source, free for the downloading and runs on Windows, Mac and Linux. The Gimp is powerful but it can’t handle 16 bit image files (yet) so I stay with the more powerful PWP. Gimp has easy to use curves and unsharp mask dialogs so you can do the entire conversion in Gimp if you like.

This next step increases the contrast a bit in the B&W image we extracted with the cyan filter. This is the PWP curves dialog. A little goes a long way. I established two control points along the curve, one for the mid-tone shadows in the bottom half and another for the mid-tone highlights in the upper half. Pull the curve down using the shadow control point and push it up with the highlight control point. Play with the curve until you like what you see. Remember that small changes can make big differences so be conservative at first.

Here’s the image with a bit of curve tweaking applied. Looks good to me.

The final step is sharpening. Here’s the PWP unsharp mask dialog. Unsharp mask gives you the most control and the best results when sharpening. I almost always use 80% with a radius of 1 or 2 (two in this case). I almost never sharpen with the threshold set to zero. I usually set it for 2 or 3 and work from there. Again, play with the sharpening until you’re satisfied with the result. Don’t be afraid to push this a little more than the rules tell you. Sometimes I’ll even sharpen a B&W image twice. In this case once was enough.

Saigon Street Scene (click to enlarge)

Here’s the final result. I think this B&W conversion could easily pass for an original B&W shot with film.

Told you this was easy. Once you understand the concept of using a colored filter to extract the best B&W image and practice a bit you’ll be able to convert your color to B&W in just a few minutes. I hope this tutorial helps and that my explanations and examples have been clear. Please feel free to comment — especially if you have questions. Hey, this was fun!

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In my last tutorial, I showed you how to expand the dynamic range of a “raw” scan using levels. Today, I’ll show you how curves can give you much finer control over image tonality.

One of the people who commented on my “found portrait” of Erin and Livvy said she liked the “raw” image better because it was softer. Good point. I like a challenge so I experimented with curves to see what I could do to change the image. I needed to bring the raw scan to life while keeping the softness of the original scan. I think this version of the portrait does just that. I’ll show you how I did it, but first let’s take the raw image through a few simple transformations.

Here’s the raw scan after I reversed the image. I set my scanner for 16 bit positive with no image correction during the scan. All of the transformations were done in 16 bit mode with Picture Window Pro using the “raw” scan.

This first transformation simply established a control point on the far left of the histogram for the black point and the far right for the white point. I moved these points to expand the dynamic range.

Here’s the image. It has plenty of contrast. Too much for a portrait like this. The directional lighting is too harsh. Let’s try another approach.

I added a third control point in the middle of the histogram and moved it to the right to brighten the mid-tones.

This image is much improved and softer. I could have done these first two operations using levels had I chosen to do so. We can do better than this.

Here’s where curves leave levels in the dust. All I did was add a fourth control point to the histogram. I used the probe tool to examine the shadow on Livvy’s cheek and establish a new control point just to the right of the shadow peak in the histogram. I didn’t move this control point. I used it to anchor the value of the raw scan so the shadows on Livvy’s cheek wouldn’t darken with a shift to the left.

Notice how the shadow peak moved to the left in the first two transformations but stayed in place this time. You can add as many control points as you like to give you finer control when you make changes to the histogram using curves. Levels limit you to the black point, the white point and mid-tones.

Small changes can make a big difference in the final outcome so it’s best to be conservative and add only what you need one step at a time. Simply adding the fourth control point was all I needed here. Notice how this last transformation kept the shape of the original histogram and expanded it more evenly.

Here’s a screen-shot of the histogram from the final image. It’s smooth with no gaps because I worked with 16 bit files. You simply cannot manipulate a B&W histogram with 8 bit files without introducing gaps that will degrade the image.

One more look at the softened portrait. I think it’s the best of the lot. Hmmm. Now I’m wondering what this image would look like if it was sepia toned? That’s another experiment for another time. I’m satisfied for now.

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