How to address this issue and enhance pixel quality by estimating or predicting the missing (zero) pixels, by using AI models

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abdullah al-dulaimi 2024-10-1

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https://ww2.mathworks.cn/matlabcentral/answers/2156845-how-to-address-this-issue-and-enhance-pixel-quality-by-estimating-or-predicting-the-missing-zero-p

回答： Aastha 2024-10-10

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Umar 2024-10-1

Hi @ abdullah al-dulaimi,

Are you trying to figure out how to enhance pixel quality by estimating or predicting the missing (zero) pixels in any given picture by using AI models in matlab?

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Answer 1

Aastha 2024-10-10

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Hi abdullah al-dulaimi,

I understand that you want to enhance the image by correcting the missing pixels in the provided image using AI models.

You can also use MATLAB's built-in functions from the Image Processing Toolbox to enhance pixel quality.

Kindly refer to the steps mentioned to enhance the image using MATLAB’s built-in functions:

1. Create a mask to identify which pixels need correction. This can be done by finding the pixels with zero values and creating a single mask for all three-color channels. However, the initial mask will have some gaps. To improve it, you can use the "imdilate" and "imfill" functions, as illustrated in the MATLAB code below:

I = imread('image.jpeg'); 
mask = double(img == 0); % Create a mask where the pixel value is 0 (black) across all color channels 
mask = (sum(mask,3) > 0); % Convert the 3D mask into a 2D mask by checking if any of the channels have a black pixel 
se = strel('square', 8); % Define a structural element (strel) for morphological operations, here a square of size 10x10 
mask = imdilate(mask, se);  % Dilate the mask to enlarge the regions marked in the mask 
mask = imfill(mask,26,"holes"); 

You may refer to MathWorks documentation for information on “imdilate” and “imfill” function.

-imdilate: https://www.mathworks.com/help/images/use-morphological-opening-to-extract-large-image-features.html

-imfill: https://www.mathworks.com/help/images/ref/regionfill.html

2. The process of predicting or estimating the missing pixels from nearby areas is called inpainting. This can be performed using the "inpaintCoherent" function in MATLAB, with the following code snippet

J = inpaintCoherent(img,mask,"Radius",5,"SmoothingFactor",5); 
% Display the inpainted image 
imshow(J); 
title( 'Inpainted Image'); 

You can experiment with the parameters of the "inpaintCoherent" function to achieve better results. Additionally, there are other inpainting methods, such as "inpaintExemplar," which you can explore .

In the image given, the pixels that need correction are not entirely black, causing the mask to miss some regions. To address this, you can interactively modify the mask by following the instructions mentioned in the link below:

 https://www.mathworks.com/help/images/interactive-image-inpainting-using-exemplar-matching-example.html

This allows you to manually select the inpainting region and adjust parameters dynamically.

On following the above-mentioned steps, I got the output image as attached:

For more information on using AI models such as GANs and Diffusion Models for inpainting in MATLAB, kindly refer to the following MathWorks documentation links:

- https://www.mathworks.com/help/deeplearning/ug/generate-images-using-diffusion.html

- https://www.mathworks.com/help/deeplearning/ug/pretrained-convolutional-neural-networks.html

I hope this helps!