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Fix Image Imperfections





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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

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Rayleigh image noise

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The Median filter - Demonstration

Filters on images


Tensorflow playground


The Averaging filter - Demonstration

Filters on images


Tensorflow playground


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



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Pixelated image (4x4)

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Depixelated image (Speckle 5x5 +0.5)

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Depixelated image (Speckle 5x5 +0.5) + Brightness (-20)

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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.

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1    1    1    2    2
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4    4    3    3    3
4    4    3    3    3



Python

Image denoising
Wiener filter
Image denoising with CNN
Denoising using Generative Adversarial Network
Remove background noise from text with CNN Auto Encoder


Contrast



Contrast applied to an image


Contrast (-20)

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Original image

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Contrast (+20)

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Brightness



Brightness applied to an image


Brightness (-20)

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Original image

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Brightness (+20)

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vibrancy of an image



Vibrancy (-70)

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Original image

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Vibrancy (+70)

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Saturation / desaturation



Desaturation (-0.5)

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Original image

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Saturation (+3)

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Exposure



Exposure (-50)

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Original image

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Exposure (+50)

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Gamma Correction



Gamma correction applied to an image


Gamma correction (+0.5)

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Original image

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Gamma correction (+2)

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Min - Max



Demonstration

Filters on images


Original image

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MIN MAX filter Matrix (7x7)

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MIN MAX filter Matrix (9x9)

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Difference



Demonstration

Filters on images


Original image

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Difference Matrix (3x3)

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Difference Matrix (5x5)

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Improved image in low-light



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Low-light Image Enhancement


Image enhancement and restoration





Original image

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Image restored

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Image enhancement and restoration


Deblur an image



SRN-Deblur
SRN-Deblur


Deblur Generative Adversarial Networks (DeblurGAN)


Deraining an image

Original image

Tensorflow playground

Derained image

Tensorflow playground


Deraining using Generative Adversarial Network


Image resized in super resolution

image super resolution


Oversized images with super resolution using generative adversarial networks



Deconvolution


Aliasing and Anti-aliasing


Chromatic Aberration


Spherical Aberration


The faults of camera sensors


Have fun testing all these corrective filters with your own images by following the links below



Sorting of waste by image processing

Medical imaging by image processing

Characterization of a face

Recognition of objects by image processing

Image processing for autonomous vehicles

Image processing for industry

Image processing for agriculture



Light Vs Colors












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