An efficient deep convolutional neural networks model for compressed image deblocking

Convolutional neural networks (CNNs) have been widely used in image processing community. Image deblocking is a post-processing strategy, which aims to reduce the visually annoying blocking artifacts that are caused by block-based transform coding at low bit rates. In recent years, CNNs based methods have been proposed to solve this classic image processing problem. In this paper, we present an efficient deep C-NNs model for image deblocking. Our model can well alleviate the conflict between bit reduction and quality preservation by taking local small patches into consideration. Our trained model can be used to deblock lossy compressed images with different quality factors. The proposed model can be easily integrated into the existing codecs as a post-processing procedure without changing the codec. Experimental results verify that our proposed model outperforms the state-of-the-art methods in both the objective quality and the perceptual quality.