Deep CNN jointing low-high level feature for image super-resolution

Xuhui Song; Weirong Liu; Jie Liu; Chaorong Liu; Chunyan Lu; Huiling Gao

doi:10.1117/12.2524412

6 May 2019 Deep CNN jointing low-high level feature for image super-resolution

Xuhui Song, Weirong Liu, Jie Liu, Chaorong Liu, Chunyan Lu, Huiling Gao

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110693O (2019) https://doi.org/10.1117/12.2524412
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

Image super-resolution methods based on forward-feed convolutional neural networks (CNN) reconstruct the image with more details and sharper texture. However, most of these methods do not consider the influence of high level semantic feature to improve image perceptual effect. In this paper, we propose a deep CNN architecture jointing low-high level feature for image super-resolution. Our method uses 17 weight layers to predict residual between the high resolution and low resolution image. And we joint the low level and high level image features to constraint the network parameters updating. Experimental results validate that our method reconstruct the high resolution images with clear edge and less warp.

Citation Download Citation

Xuhui Song, Weirong Liu, Jie Liu, Chaorong Liu, Chunyan Lu, and Huiling Gao "Deep CNN jointing low-high level feature for image super-resolution", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110693O (6 May 2019); https://doi.org/10.1117/12.2524412

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