The main goal of this work is to deblock the compressed JPEG using denoising convolutional neural networks.
Deblocking is an image filter that smooths the sharp edges of a decoded compressed image to enhance visual quality and prediction performance. Here in this work, image deblocking is implemented using deep learning.
Training a denoising convolutional neural network (DnCNN) will take place and then using the network to reduce JPEG compression artifacts in an image is performed. Quality of the deblocked images is quantified by four metrics namely SSIM, PSNR, NIQE and BRISQUE.
Keywords: Deblocking, DnCNN, Deep Learning, Compression.
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