Hi,
I understand that you are working with unclear or low-resolution images and would like to enhance their quality using a super-resolution algorithm. You are also looking for a clear explanation of the steps involved in applying such an algorithm.
I assume you are referring to single-image super-resolution and aim to apply it using MATLAB, potentially using available models like SRCNN.
In order to apply super-resolution on low-resolution images, you can follow the below steps:
Step 1: Load the low-resolution image
Use the "imread" function to read your input image into MATLAB.
imgLR = imread('your_low_res_image.jpg');
Step 2: Convert the image to YCbCr color space
Super-resolution models like SRCNN work on the luminance channel.
imgYCbCr = rgb2ycbcr(imgLR);
imgY = imgYCbCr(:,:,1); % Extract luminance channel
Step 3: Resize the image (optional pre-upscaling)
Use "imresize" to upscale the image, which serves as input to the model.
imgYup = imresize(imgY, 2, 'bicubic');
Step 4: Apply a pretrained super-resolution model
Use a model like SRCNN available in MATLAB via "deep learning toolbox".
net = importONNXNetwork('srcnn.onnx','OutputLayerType','regression');
imgYupSingle = single(imgYup)/255;
imgSR = predict(net, imgYupSingle);
Step 5: Combine and visualize the result
Merge the enhanced luminance with original chrominance and convert back to RGB.
imgYCbCr(:,:,1) = uint8(imgSR*255);
imgHR = ycbcr2rgb(imgYCbCr);
imshow(imgHR);
Refer to the documentation of "imresize" function to know more about interpolation options:
Refer to the documentation of "importONNXNetwork" to learn how to load pretrained models:
Refer to the documentation of "predict" for applying the network to an image:
Hope this helps!
