Learning Local Distribution for Extremely Efficient Single-Image Super-Resolution

Electronics

Bolun Zheng¹  Aiai Huang¹  Wei Wu¹  Wen Xu¹  Chenggang Yan¹ 

[1] School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;

关键词: image super-resolution;    local distribution model;    extreme efficient;    multi-layer neural network;   

DOI  :  10.3390/electronics11091348

来源: DOAJ

【 摘 要 】

Achieving balance between efficiency and performance is a key problem for convolution neural network (CNN)-based single-image super-resolution (SISR) algorithms. Existing methods tend to directly output high-resolution (HR) pixels or residuals to reconstruct the HR image and focus a lot of attention on designing powerful CNN backbones. However, this reconstruction way requires the CNN backbone to have good ability to fit the mapping function from LR pixels to HR pixels, which certainly held these methods back from achieving extreme efficiency and from working in embedded environments. In this work, we propose a novel distribution learning architecture to estimate the local distribution and reconstruct HR pixels by sampling the local distribution with the corresponding 2D coordinates. We also improve the backbone structure to better support the proposed distribution learning architecture. The experimental results demonstrate that the proposed method achieves state-of-the-art performance for extremely efficient SISR and exhibits a good balance between efficiency and performance.

【 授权许可】

Unknown