Image Analyzer examples

For Most Help, look in the status bar in the bottom of the program window for hints on what the tools do. Some features are described with examples in this page. It is a work in progress, and answers to questions I receive might be added.

Adaptive noise removal
Auto color correction
Fourier transformation
PNG versus GIF and JPEG
Red eye removal
Restoration by deconvolution
Texture synthesis

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Auto color correction

Auto color correction adjusts black/white point, gamma and saturation. This will improve the contrast and enhance details in the image. Black/white point and gamma are adjusted in the same way as when you press the Auto button in Color mapper. The adjustment is based on an analysis of the image. By making a selection before clicking Auto color correction you can tell the program to only analyze that part of the image. You would probably want to do that when correcting a picture with e.g. a black background.

Original

Corrected

Red eye removal

Make a selection around the red part and click Red eye removal (Alt-E). The selection does not have to be very accurate, but making it too large might result in loss of color around the eye.

Restoration by deconvolution

This feature will allow advanced reconstruction of blurred images. Both motion blur, out-of-focus blur and other errors are handled. In the example below, the camera was moved what correspond to about 17 pixels while the picture was taken. This distance can be estimated by looking at the details in the background: A tree is approximately 17 pixels wide. Note that the bird is moving, and therefore cannot be reconstructed using the same model as the background.
If the motion is neither horizontal nor vertical, the picture must be rotated before deconvolution. This might, however, reduce the quality of the reconstruction somewhat.





Clip from original photograph from Ching-Kuang Shene


Restoration after 8 CGLS iterations.

For out-of-focus blur, the Circular blur model will probably give the best results. The default setting will sharpen an image which is slightly unsharp. To find out which radius gives the best result use the Test button. This will produce a number of reconstructions with different filter radius. Enter an iteration count (~12), click Test, enter the lower and upper bound for the radius and the number of reconstructions to generate. If the result is too grainy then reduce the number of iterations.

Original

Corrected, circular blur 12 iterations, radius=3.6

If the convolution filter matrix is known it can be given when selecting Matrix filter or Matrix file:

F is a matrix and F(r,c,d) is the expression for the element (r,c) where (0,0) is the center. d is the distance from the center, d=||r-c||. This makes it easier making circular symmetric filters.

If you are wondering what interp() is you can just use the Expression evaluator (in help menu or F11) to plot it using the command "plot(interp(x),x)". Note that you should open the deconvolution window first to get the function defined. It just a very simple function used for interpolation when constructing the circular convolution filter.

Matrix files should be in either MAP, Matlab MAT or text format.
An example of a filter file can be found here:

5star.txt
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When used for deconvolution, this matrix will result in sharpening
of the image.

Very short mathematical description of the algorithm
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Adaptive noise removal

Adaptive noise removal (Alt-A) can remove high-frequency noise from most images. Unlike the smoothing algorithm for noise reduction found in many image processing programs, Adaptive noise removal works without unsharpening edges in the image.
Not only will noise reduction improve the visual impression of an image, it will also make JPEG compression more effective producing smaller files with the same quality selection.
The example is the result of applying the filter to the deconvolution image above using the default options.

Texture synthesis

Texture synthesis (Special menu) is a tool for generating textures from a sample or filling holes in an image.
Texture generation is performed by creating a blank image (File | New) and opening one or more sample images and selecting them in the Texture sources box. Destination mask tells the program what part of the image is known. In texture generation we say that only the center pixel is known and all other pixels should be synthesized. Method should be Source match and the Texel size the approximate size of the pattern features. In the example below a large brick is about 45 pixels wide. If the random error is set larger than 0, some more computation time is required but the program might be better at synthesizing high-frequency elements.

Sample	Sample
Synthesized texture	Synthesized texture

If part of an image is covered (e.g. by text) or contains an unwanted object, texture synthesis can sometimes be used for restoration. The traditional way of solving such a problem is by cloning and retouching by hand, but texture synthesis can do it automatically. In the example the bird from above was removed by synthesis using a texture sample taken from the image itself. A selection around the bird was made, Destination mask set to Selection and Target to Inside. The Source match method and Texel size equal to 9 was used.

Texture sample	Fixed image

The other two methods are also for hole filling. Instead of sample textures they only use the border of the hole for the synthesis. These methods are much faster but will only produce good results if the texture is very simple. If you want to define the target area more precise than it is possible with a rectangular selection you can make a grayscale image the same size as the synthesis image and mark the target area with white. This mask image will then appear in the Destination mask list.
A note about computation time: Texture synthesis can be very time consuming. To speed up the process use textures as small as possible (maybe 64 x 64 pixels) and select a small texel size. Using grayscale images and textures is the fastest and will reduce the time of experiments. Also turn the progress display off while synthesizing large textures.

Fourier transformation

I have been asked about saving the result of the Fourier transform and editing it in another program. It is simply not possible to save the result of a Fourier transform to a standard image file. A Fourier transform produces complex numbers whereas e.g. BMP files can only contain ordinary integer numbers in the range [0; 255]. If you want to edit the Fourier image and be able to do an inverse transform, you have to either use the editing function in Image Analyzer or edit the MAP file you get when saving the Fourier transform. In Image Analyzer you can use the eraser tool or make a selection and hit Ctrl-Delete to remove frequency components, or you can use the Frequency domain filter option in the Filters menu.

PNG versus GIF and JPEG

Why is there no GIF under File format options so that I can make transparent GIFs etc.?
Most people only use GIF because of ignorance. There is really no reason to use the old GIF format anymore. Use PNG instead! All modern browsers and graphics programs support PNG, it has better compression (produces smaller files) and it supports more color formats (e.g. 24 bit). The 256 x 256 grayscale image in the top of this page takes up 63 kb as GIF and only 41 kb as PNG. This means 30% disk space saved and 30% less download time.

If using the lossy version of PNG the size reduction is even greater. With a quality setting of -7 the PNG file size is 21 kb - only one third of the GIF file and without significant visual quality loss. Lossy JPEG can produce even smaller files, and is in most cases better for photographic images. But JPEG does not support transparency, and is not very good at compressing images with sharp edges like diagrams and screenshots.
Note that the lossy PNG compression is not as effective with 256 color palette images as with grayscale and 24 bit images.