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Image processing involves enhancing the content of an image to extract information from it.
To process an image is to add meaning to it.
The noise
Original image
Rayleigh image noise
The Median filter - Demonstration
The Averaging filter - Demonstration
Speckle denoising
The goal is to replace the statistically outlying central pixel value with the average of the neighbors.
For this, we calculate the mean and the standard deviation of the values of the neighbors of the central pixel.
We then calculate the deviation between the central pixel and this average.
If this deviation is greater than the standard deviation (within a factor), then we consider that the value of the central pixel is outlying and we replace it with the average of the neighbors.
Speckle Depixelization
Original image
Pixelated image (4x4)
Depixelated image (Speckle 5x5 +0.5)
Depixelated image (Speckle 5x5 +0.5) + Brightness (-20)
KUWAHARA denoising
The Kuwahara filter is a nonlinear smoothing filter used in image processing for adaptive noise reduction.
The Kuwahara filter applies smoothing to the image while preserving edges.
1 1 1 2 2
1 1 1 2 2
4 4 0 2 2 /60
4 4 3 3 3
4 4 3 3 3
Python
Contrast
Contrast (-20)
Original image
Contrast (+20)
Brightness
Brightness (-20)
Original image
Brightness (+20)
vibrancy of an image
Vibrancy (-70)
Original image
Vibrancy (+70)
Saturation / desaturation
Desaturation (-0.5)
Original image
Saturation (+3)
Exposure
Exposure (-50)
Original image
Exposure (+50)
Gamma Correction
Gamma correction (+0.5)
Original image
Gamma correction (+2)
Min - Max
Demonstration
Original image
MIN MAX filter Matrix (7x7)
MIN MAX filter Matrix (9x9)
Difference
Demonstration
Original image
Difference Matrix (3x3)
Difference Matrix (5x5)
Improved image in low-light
Image enhancement and restoration
Original image
Image restored
Deblur an image
Deraining an image
Original image
Derained image
Image resized in super resolution
Have fun testing all these corrective filters with your own images by following the links below
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