Deep learning method for Single Image Super Resolution

Capstone project for a course on machine learning and deep learning

• This is an implementation of Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.Used left-thomas's implementation of SRGAN.

• Trained a modified SRGAN model using google colab, with the VOC2012 dataset(approx 1000 images) for different upscale values.

• Tried to modify the network to be able to take in any upscale value (not fractional), instead having only exponentials of 2 as is in the paper, upon doign so, noticed that the modification resulted in more number of parameters than the original model, for upscale values like 4,6,8 etc.

• This is why I think it resulted in more number of parameters:

tl;dr

Original model uses lesser number of "channels" to upscale the image when compared to the modified model. Read this to understand more

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Some results from the trained model

Modified SRGAN model, trained for 930 epochs:

input :

output: PSNR:27.07

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input :

output: PSNR:20.50

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input :

output: PSNR:28.57

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Pre-trained ESRGAN:

input :

output: