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Photoshop CS5 One-on-One: Advanced, the second part of the popular and comprehensive series, updated for CS5, follows internationally renowned Photoshop guru Deke McClelland as he dives into the workings of Photoshop. He explores such digital-age wonders as the Levels and Curves commands, edge-detection filters, advanced compositing techniques, vector-based text, the Liquify filter, and Camera Raw. Deke also teaches tried-and-true methods for sharpening details, smoothing over wrinkles and imperfections, and enhancing colors without harming the original image. Exercise files accompany the course.
Recommended prerequisite: Photoshop CS5 One-on-One: Fundamentals.
The topic of this chapter is smooth and surface, and the idea is that there're certain kinds of surface details, such as digital noise, for example, that you need to smooth away inside of an image, there's other times that you might want to enhance or add surface details. We're going to see how this works with this butterfly right here. Go figure, I'm obviously something of a frustrated nature photographer. I especially like the idea of macro photography, where you can take these delicate small creatures and blow them up, that is make them larger, not explode them, here inside Photoshop.
The name of this image is Long feelers.jpg. It's found inside the 16_smooth folder. This image contains all kinds of noise. We've got color noise. We've got luminance noise inside the shadows. I'll explain the differences between those kinds of noise in just a moment. We also have all kinds of wandering colors and exaggerated saturation levels inside the shadow details as well. That kind of stuff is very common, for reasons I will explain. We're going to take this image and we will ultimately sharpen it as well, so you want to smooth out all the imperfections inside the image before you sharpen it.
We'll end up with this composition right here, and it's called Final feelers.psd, also found inside the 16_smooth folder. It's in much better shape, as you can see. We don't have nearly the color problems in the shadows. We've got much better detail, as well it's holding up better. It's not a noiseless image by any means. You don't entirely get rid of the noise, because if you did that, you'd get rid of some details as well. Instead, it's all about mitigating that noise and mitigating the color problems as well. Obviously, it requires a few layers, but it's not that tough, as we'll see.
And then finally, just for fun, we're going to add a paper texture in order to create a painterly effect right here, and the name of this composition is Fly paper.psd. Note by the way that it relies on a couple of filter effects that are set on separate layers. So in the background here, we have a flattened version of that final composition, that we can see if I Alt+click or Option+click on that eyeball in front of the Background layer. Then we just add in a few additional layers, apply some Blend modes, and we get this effect.
So very easy to pull off this kind of stuff. Also, fairly satisfying as well, you may find yourself inspired to try out other techniques. All right! So I'm going to switch back to Long feelers.jpg. I'm going to zoom in on this image, so that we can see it in all of its tragic clarity here. Notice that we have a ton of noise going on, which are of course random variations in terms of color and luminance levels between neighboring pixels. I'm going to go ahead and zoom in to 200%, so that we can see that noise up close and personal.
Now, I should tell you that I've done everything in my power to make sure this noise is every bit as bad as it can be. I haven't added any noise whatsoever. This is all noise that was captured along with the image. I've just done everything in my power to nurture it along, so that you can see it very easily inside the videos, because otherwise, thanks to the way video compression works and so on, we could end up losing some of this noise detail. Also, by the way, I want this to be the noisiest image you ever run into. That way, if you're able to pull off the techniques I show you here, then you can apply these same techniques with even better results to your less noisy images.
By the way, they don't have to be insects. Doesn't matter what the subject of the image is, it could be a landscape, it could be a portrait, these same techniques apply. All right! So two varieties of noise; we've got color noise, which are random variations in terms of Hue and Saturation between neighboring pixels. So for example, in this creature's compound eyes, we've got dark blue, we've got light blue, we've got violet and purple and red and brown, all kinds of colors going on. You can see a wild variety of colors in the background as well.
It's a kind of color static that's going on. It shouldn't be there, because it wasn't there in real life. This is something that the camera made up. It didn't find all these little magical differently colored squares in the air and captured them, it is a function of the way that the camera captured the scene in the first place and then converted the scene into an RGB color image, which brings us to our second kind of color here, which is luminance noise, random variations in terms of the brightness of the pixels.
We have all kinds of luminance noise showing up inside the shadow detail, both along this creature's neck or whatever you call it on a butterfly. Also, down here, notice these shadows have lots and lots of noise in them. And by shadows, I mean the darkest details inside the image. This is very common where digital photographs are concerned, and here is why. When your digital camera captures an image, it actually captures grayscale information. This is the way it works with 99.9 % of the digital cameras out there.
There are a few exceptions, but very few. So it captures this grayscale image that comes with filtering information, so that the Camera or Photoshop can convert that image into RGB. Now, along the lines, the image has to be brightened, because it starts off very dark. It's captured by the camera. It is a very dark photograph. By the way, the camera does this automatically, when it converts the image to JPEG, or if you capture a RAW image, then something like Camera RAW does this automatically. By the way, we will be discussing Camera RAW, which is a utility that ships along with Photoshop CS5, very powerful little utility by the way, very, very useful.
We'll be talking about it at the end of this course in the final chapter of this course. But in the meantime, either the Camera or Camera RAW are going to automatically elevate the Midtones inside the image in order to make the image brighter, so that it looks the way that we perceive the scene. In doing so, the Camera or Camera RAW ends up compressing the heck out of the highlight information. So it takes all this wealth of luminance information and turns it into highlight data, which means that you have more than enough information to cover those highlights.
So it's unlikely that you're going to see much in the way of luminance noise in the highlights, but it has to stretch out the data that's associated with the shadows. And as a result of extending that luminance information, you end up revealing luminance noise. All right! So that's where you want to look, inside your images. If you're worried about noise, which well, you should be, you should look in those shadows. Also, by the way, the shadows have a tendency to turn the wrong color sometimes. This is a less common problem, but it still does happen.
So notice down here in this green stuff, that the butterfly is standing on, that we have these warmer shadows. Then inside of the butterfly, we end up having these very cool shadows, that are trending toward blue or even violent. Again, a very common problem. This is also a common problem with scanning photographs by the way. So those are the issues that we're going to be dealing with. We're going to start things off using a very powerful Noise Removal filter inside of Photoshop. It's available here under the Filter menu.
You go down to Noise and you choose this guy right there, Reduce Noise, just as we will do in the very next exercise.
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