Single image super-resolution using a deep encoder-decoder symmetrical network with iterative back projection

NEUROCOMPUTING

卷:282

Article

Liu, Heng¹  Han, Jungong²  Hou, Shudong¹  Shao, Ling³  Ruan, Yue¹ 

[1] Anhui Univ Technol, Sch Comp Sci & Technol, Maanshan 243032, Peoples R China

[2] Univ Lancaster, Sch Comp & Commun, Lancaster LA1 4YW, England

[3] Univ East Anglia, Sch Comp Sci, Comp Vis & Machine Learning, Norwich NR7 TJ4, Norfolk, England

关键词: Single image super-resolution;    Deep encoder-decoder;    Symmetrical network;    Iterative back projection;   

DOI  :  10.1016/j.neucom.2017.12.014

来源: Elsevier

【 摘 要 】

Image super-resolution (SR) usually refers to reconstructing a high resolution (HR) image from a low resolution (LR) image without losing high frequency details or reducing the image quality. Recently, image SR based on convolutional neural network (SRCNN) was proposed and has received much attention due to its end-to-end mapping simplicity and superior performance. This method, however, only using three convolution layers to learn the mapping from LR to HR, usually converges slowly and leads to the size of output image reducing significantly. To address these issues, in this work, we propose a novel deep encoder-decoder symmetrical neural network (DEDSN) for single image SR. This deep network is fully composed of symmetrical multiple layers of convolution and deconvolution and there is no pooling (down-sampling and up-sampling) operations in the whole network so that image details degradation occurred in traditional convolutional frameworks is prevented. Additionally, in view of the success of the iterative back projection (IBP) algorithm in image SR, we further combine DEDSN with IBP network realization in this work. The new DEDSN-IBP model introduces the down sampling version of the ground truth image and calculates the simulation error as the prior guidance. Experimental results on benchmark data sets demonstrate that the proposed DEDSN model can achieve better performance than SRCNN and the improved DEDSN-IBP outperforms the reported state-of-the-art methods. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free