# Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network
###### 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.  
computer vision; deep learning; convolutional neural network; image processing; image restoration; single image artifacts reduction; dense networks; residual networks; channel attention networks +
Footnote †: journal: Medical  
cascading structures, CNN-based artifact reduction (AR) methods can achieve visual enhancement in terms of peak signal-to-noise ratio (PSNR) [26], PSNR including blocking effects (PSNR-B) [27; 28], and structural similarity index measures (SSIM) [29].  
Despite the developments of AR, most CNN-based approaches tend to design the heavy network architecture by increasing the number of network parameters and operations. Because it is difficult to deploy such heavy models on hand-held devices operated on low complexity environments, it is necessary to design the lightweight AR networks. In this paper, we propose a lightweight CNN-based artifacts reduction model to reduce the memory capacity as well as network parameters. The main works of this study are summarized as follows:  
* To reduce the coding artifacts of the compressed images, we propose a CNN based densely cascading image restoration network (DCRN) with two essential parts, densely cascading feature extractor and channel attention block.
* Through a various ablation study, the proposed network is designed to guarantee the optimal trade-off between the PSNR and the network complexity.
* Compared to the previous method, the proposed network is designed to obtain comparable AR performance while utilizing the small number of network parameters and memory size. In addition, it can provide the fastest inference speed, except for initial AR network [30].
* Compared to the latest methods to show the highest AR performances (PSNR, SSIM, and PSNR-B), the proposed method can reduce the number of parameters and total memory size maximum by 2% and 5%, respectively.  
The remainder of this paper is organized as follows: in Section 2, we review previous studies related to CNN-based artifact reduction methods. In Section 3, we describe the proposed method. Finally, in Sections 4 and 5, we present the experimental results and conclusions, respectively.