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Image manipulation

About 99% of images we see in magazines wil have been digitally manipulated, and there is a range of software available for doing that.

Digital images are made up of pixels, so complex processing can be applied by manipulating the values of those pixels, either individually or by applying a formula to all of them.

Statistical operations

Starting from the intensity histogram of an image gives us the distribution of intensity values across an image. We can then create a bi-level threshold by picking a place on the diagram and making every pixel with an intensity value below that a 0 (black) and give everything else an intensity value of 1 (white). The thresholds are normally chosen according to minima in the histogram. You can also tell from the intensity histogram what kind of image this is. A dark image will have peaks in the low end of the chart, while a light one will have peaks at the high end. A low contrast picture will have all of its intensity values in a small range, while a high contrast picture will have a more spread out histogram. To increase the contrast you can stretch the intensity histogram out (known as linear contrast stretching), or lighten a dark image by moving the peaks.

Gamma correction

When an intensity is passed to an output device, there is a non-linear relationship between the intensity value and the output value. So if the intensity increases by 10 times, the output intensity may increase by more than that... Say 100 times for example. So a nice linear grey scale may come out looking not very linear. Gamma correction is applied to correct this.

Intensity = Voltage of the device gamma where gamma is a value for the particular device. It will vary between devices, but is generally between 2.3 and 2.4. 

Pixel group processing 

Uses the  values of the neighbouring pixels to determine the final value of a given pixel. Done by applying a 'convolution matrix' to the values. Doing this can give smoothing, sharpening, edge detection and noise removal, depending on the values used in the matrix. So, you take a 3x3 block of pixels and calculate the value of the one in the middle by applying a 3x3 set of weights to that block.

You can average out the values, by applying a constant 1/9 weight across the matrix. That gives you a smoothing effect. It's a simple thing to implement, but gives some pretty crude results. An alternative is to apply a weighting based on a Gaussian distribution, and this is known as Gaussian smoothing or blur. There's a good outline of the procedure here. (I'm pretty sure I did all about Gaussian distributions in my engineering degree - there are faint bells going off, but nothing particuarly useful!)


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