Deep dual-domain semi-blind network for compressed image quality enhancement

Compressed image quality enhancement has attracted a large amount of attention in recent years. In general, the primary goal of compression is artifact reduction to produce a higher-quality output from a low-quality input. Information loss and compression artifacts are mostly due to quantization. The quantization matrix is determined by the compression quality factor (QF). However, there has thus far been little related research to estimate the compression quality factor for JPEG images. To address this issue, in this paper, we propose a deep dual-domain semi-blind network (DSN) that combines compression quality factor detection and compressed image quality enhancement. Specifically, a quality factor detection (QFD) module is designed to capture contextual information of the space and frequency domains. Furthermore, we build a novel deep dual-domain compressed image quality enhancement network to remove the compression artifacts by using the prior in terms of both the discrete cosine transform (DCT) and pixel domains. Different from previous algorithms, our proposed approach can remove compression artifacts generated at different quality factors by inferring the image quality. Experimental results demonstrate the superiority of our deep dual-domain semi-blind network over state-of-the-art methods in terms of objective quality and visual results.