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In highly compressed images, i.e. quality factor <inline-formula> <tex-math notation="LaTeX">$q \leq 10$ </tex-math></inline-formula>, JPEG compression causes severe compression artifacts including blocking, banding, ringing and color distortion. The compression artifacts seriously degrade image quality, which is not conducive to subsequent tasks, such as object detection and semantic segmentation. In this paper, we propose a weakly connected dense generative adversarial network for artifacts removal of highly compressed images, named WCDGAN. WCDGAN has three main ingredients of mixed convolution, weakly connected dense block (WCDB), and mixed attention. In the loss function, we add a perceptual loss to generate photo-realistic images with compression artifact removal. Experimental results show that WCDGAN successfully removes compression artifacts and produces sharp edges, clear textures and vivid colors even in highly compressed images. Moreover, WCDGAN outperforms state-of-the-art methods for compression artifact removal in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).

WCDGAN: Weakly Connected Dense Generative Adversarial Network for Artifact Removal of Highly Compressed Images