**5. Conclusions**

Image compression leads to undesired compression artifacts due to the lossy coding that occurs through quantization. These artifacts generally degrade the performance of image restoration techniques, such as super-resolution and object detection. In this study, we propose a DCRN, which consists of the input layer, a densely cascading feature extractor, a channel attention block, and the output layer. The DCRN aims to recover compression artifacts. To optimize the proposed network architecture, we extracted 800 training images from the DIV2K dataset and investigated the trade-off between the network complexity and quality enhancement achieved. Experimental results showed that the proposed DCRN can lead to the best SSIM for compressed JPEG images compared to that of other existing methods, except for IDCN. In terms of network complexity, the proposed DCRN reduced the number of parameters by as low as 72%, 5% and 2% compared to DnCNN, IDCN and RDN, respectively. In addition, the total memory size was as low as 91%, 41%, 17% and 5% of that required for DnCNN, DCSC, IDCN and RDN, respectively. Even though the proposed method was slower than ARCNN, it's PSNR, SSIM, and PSNR-B are clearly better than those of ARCNN.

**Author Contributions:** Conceptualization, Y.L., B.-G.K. and D.J.; methodology, Y.L., B.-G.K. and D.J.; software, Y.L.; validation, S.-h.P., E.R., B.-G.K. and D.J.; formal analysis, Y.L., B.-G.K. and D.J.; investigation, Y.L., B.-G.K. and D.J.; resources, B.-G.K. and D.J.; data curation, Y.L., S.-h.P. and E.R.; writing—original draft preparation, Y.L.; writing—review and editing, B.-G.K. and D.J.; visualization, Y.L.; supervision, B.-G.K. and D.J.; project administration, B.-G.K. and D.J.; funding acquisition, E.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) gran<sup>t</sup> funded by the Ministry of Science and ICT (Grant 21PQWO-B153349-03).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
