Image recovery technology is an important processing technology in image processing field, similar to other basic image processing techniques, such as image enhancement, the technique is also aimed at obtaining some improvement of visual quality, and the image restoration process needs to be done according to the specified image degradation model. The degraded images degraded or deteriorated in some cases were recovered according to the degenerate model to obtain the original, non-degraded original images. In other words, the process of image restoration is actually the improvement of degraded image quality, and the improvement of image quality can improve the image visually.
Here are some of the functions in MATLAB's own library of functions,
Call it out and study to understand
respectively, motion blur, disc blur, passivation blur.
I=imread (' src.png '); subplot (221); Imshow (I); title (' Src '); H=fspecial (' Motion ', 20,30); % motion convolution of the offset matrix motionblur=imfilter (I,H); % convolution subplot (222); Imshow (Motionblur); title (' Motionblur ') h1=fspecial (' Disk ', 10); % disk-shaped offset matrix disk=imfilter (I,H1); % convolution subplot (223), imshow (disk), title (' Disk ') h2=fspecial (' Unsharp '); % passivation Fuzzy offset matrix unsharp=imfilter (I,H2); % convolution subplot (224); Imshow (Unsharp); title (' Unsharp ')
It's so shy to put my own handsome picture on the damn.
The main is the understanding of several functions inside.
Let me explain it as I understand it.
H-Matrix: It's easy to see that the image is shifted right up and superimposed, which is convolution.
Blur photo with long exposure time during motion simulation
H1 Matrix: It can be seen that the image is translated into a circle after each point overlay.
Analog aperture not adjusted good shots taken out of the picture
H2 matrix: High-pass filter
It could be a transmission or a coding problem.
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Degraded image restoration based on MATLAB (i.)------image degradation processing