For a better performance in single image super-resolution(SISR), we present an image super-resolution algorithm based on adaptive dense connection (ADCSR). The algorithm is divided into two parts: BODY and SKIP. BODY improves the utilization of convolution features through adaptive dense connections. Also, we develop an adaptive sub-pixel reconstruction layer (AFSL) to reconstruct the features of the BODY output. We pre-trained SKIP to make BODY focus on high-frequency feature learning. The comparison of PSNR, SSIM, and visual effects verify the superiority of our method to the state-of-the-art algorithms.
@article{arxiv.1912.08002,
title = {Adaptive Densely Connected Super-Resolution Reconstruction},
author = {Tangxin Xie and Xin Yang and Yu Jia and Chen Zhu and Xiaochuan Li},
journal= {arXiv preprint arXiv:1912.08002},
year = {2019}
}