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Real focus happens inside the camera's lens element. The sharpening features in Photoshop CS3 exaggerate the contrast along edges in a photograph to transform a well-focused image into an outstanding image. In Photoshop CS3 Sharpening Images, Deke McClelland teaches a host of sharpening and noise reduction techniques, including using filters such as Unsharp Mask, Smart Sharpen, High Pass, and Reduce Noise. The training teaches the essentials of sharpening, including what it does, why it's important, and how the filters function. Plus, the training covers Deke's recommended best practices, including the four distinct varieties of sharpening, which can be used independently or in combination with each other. Photoshop CS3 Sharpening Images is about how to transform images from looking good to looking their absolute best. Exercise files accompany the course.
Now before I explain how Camera RAW Sharpening functions work, I want to give you a sense of why they exist in the first place. They are not designed to sharpen the perceived focus of the image; they are not designed to make the image pop on the printed page. They are rather designed to compensate for the photographic process, specifically for the conversion of the analog world to the digital world. When you convert an analog image or the real world, which is analog after all, to a massive digital pixels, you are introducing anti-alias thing, and interpolation and all sorts of varieties of softening.
So I am just going to give you a sense of one conversion that introduces softness to the image, so you just have a sense of what is going on and than we'll dive into the functions. Now I am looking at this image its called Sensor grid.PSD, that's found if you want to open it up, it's found inside the 05_For_Source folder and this is a diagram,. Obviously it's not a real photograph. This is a diagram of a detail from an image sensor, that chip that's inside of a digital camera. And the image sensor, this would be a really super magnified version.
The image sensor contains a bunch of little sensors. So the word sensor has double meaning here. In the case of a 10 mega pixel camera for example there would be 10 million sensors on the chip, each of which is capturing a pixel of data. The problem is that each of the sensors can only in a typical camera, each one of the sensors can only record luminance information, just the lightness information. It can't record their over all color information. So there needs to be some kind of filtering applied over it, in order to capture just the color and if I go over here to the Layer Comps palette and I switch over to Color Filter Array, we can see the striping that is applied.
This is called striping, where each one of the sensors gets a little drop of colored resin on it essentially, that acts as a light filter. So this way, even though we are just recording the luminance information, we have this common Bayer pattern and what I mean by that is, what we are seeing here is a Bayer pattern and that is the most common solutions where digital cameras are concerned, just like something like 99% of the cameras out there use a Bayer pattern. This Bayer pattern favors green. So what we are seeing is in any block of 2x2 sensors and bear in mind, this is just a detail of the overall chip.
The overall chip would have 10 million sensors on it, lets say, this one just has a handful. We are in any group of four of these sensors, two wide by two tall, we have two sensors that are filtered with green and than one is filtered with red and one is filtered with blue. And there is two reasons for this. One is that our eyes naturally respond to green light more than red light and way more than blue light. And the other reason is that the green resin does less to filter the light than the red or the blue resin.
So we are doing less light filtering and that way we are capturing more light information, more detail information. So that is why we are doubling up on the green, half as many reds and blues. Now the problem is of course is that all we are getting, all the digital camera is capturing and this is the case with the RAW image by the way, this is what is coughed up with the RAW file format image. Although, we are getting is a pixel that's a green pixel followed by a pixel that's a blue pixel and then a row of those and then next row of red pixel followed by a green pixel and a row of those. Then we are back to green and blue.
So we never have a full color pixel anywhere inside the RAW image. What Camera RAW is responsible for doing is de-mosaicing this image so it is converting it from this mosaic sensor pattern that we are seeing right here. It's de-mosaicing, which is to say, it is averaging; it is applying the weighted average in order to find out what the exact color of the pixel is. So for the example in the case of, lets say this pixel right there, Camera RAW has to figure out what the real color of that pixel is. So it weighs in the fact that this happens to be a green pixel on the first place and then it surrounded by eight other pixels, it goes ahead and creates an average of these nine pixels together, taking into consideration what each of the original colors of the pixels is, and then it manufactures the correct color.
I say correct in big quote fingers because it's not after all exactly the correct color, it's an averaged pixel. But this averaging is known generally as interpolation and so it introduces softening into the image. Imagine for example that you have a detail inside your photograph that really only measures one pixel wide. What happens? It ends up sloughing over into the other pixel shifts a little bit and that creates the appearance of softness. It's a very slight appearance of softness and that's why you have to take it easy with your sharpening inside of Camera RAW as we'll see.
You are also applying a little bit of noise reduction, a few other adjustments as well and that is all designed in order to compensate for the photographic experience. So once again don't try to sharpen the over all condition of the photograph, just try to sharpen for the source and that's what we will be doing in subsequent exercises.
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