*Article* **Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks**

**Seonjae Kim 1, Dongsan Jun 2,\*, Byung-Gyu Kim 3, Hunjoo Lee 4 and Eunjun Rhee 4**


Daejeon 34129, Korea; hjoo@etri.re.kr (H.L.); ejrhee@etri.re.kr**\***Correspondence:dsjun9643@kyungnam.ac.kr

**Abstract:** There are many studies that seek to enhance a low resolution image to a high resolution image in the area of super-resolution. As deep learning technologies have recently shown impressive results on the image interpolation and restoration field, recent studies are focusing on convolutional neural network (CNN)-based super-resolution schemes to surpass the conventional pixel-wise interpolation methods. In this paper, we propose two lightweight neural networks with a hybrid residual and dense connection structure to improve the super-resolution performance. In order to design the proposed networks, we extracted training images from the DIVerse 2K (DIV2K) image dataset and investigated the trade-off between the quality enhancement performance and network complexity under the proposed methods. The experimental results show that the proposed methods can significantly reduce both the inference speed and the memory required to store parameters and intermediate feature maps, while maintaining similar image quality compared to the previous methods.

**Citation:** Kim, S.; Jun, D.; Kim, B.-G.; Lee, H.; Rhee, E. Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks. *Appl. Sci.* **2021**, *11*, 1092. https://doi.org/10.3390/app11031092

Received: 10 December 2020 Accepted: 22 January 2021 Published: 25 January 2021

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**Keywords:** deep learning; convolutional neural networks; lightweight neural network; single image super-resolution; image enhancement; image restoration; residual dense networks
