*Article* **Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network**

**Yooho Lee 1, Sang-hyo Park 2, Eunjun Rhee 3, Byung-Gyu Kim 4,\* and Dongsan Jun 1,\***


**Abstract:** Since high quality realistic media are widely used in various computer vision applications, image compression is one of the essential technologies to enable real-time applications. Image compression generally causes undesired compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a densely cascading image restoration network (DCRN), which consists of an input layer, a densely cascading feature extractor, a channel attention block, and an output layer. The densely cascading feature extractor has three densely cascading (DC) blocks, and each DC block contains two convolutional layers, five dense layers, and a bottleneck layer. To optimize the proposed network architectures, we investigated the trade-off between quality enhancement and network complexity. Experimental results revealed that the proposed DCRN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed joint photographic experts group (JPEG) images compared to the previous methods.

**Keywords:** computer vision; deep learning; convolutional neural network; image processing; image restoration; single image artifacts reduction; dense networks; residual networks; channel attention networks
