Learning Enriched Features for Image Super Resolution

Abstract

In recent years, significant progress has been made in image super-resolution (SR) methods based on convolutional neural networks. However, most of them do not fully utilize multi-scale feature correspondence in the image SR process, resulting in blurred and artifact detail restoration, especially for SR tasks with larger scaling factors (i.e. ×4 and ×8). A multi-scale feature-enhanced SR network (MFENet) is proposed to solve the problems mentioned above. Specifically, the super-resolution method is enhanced by utilizing multi-scale feature correspondence in two ways. On the one hand, we propose an effective multi-scale non-local attention module and it can fully exploit a large number of low-level features at different scales of a single low-resolution (LR) feature map by exhaustively evaluating the correlations in local feature, in-scale non-local feature, and cross-scale non-local feature. On the other hand, the mapping from high-resolution (HR) to LR images is explored by a feedback branch we introduced, that provides an additional constraint for images reconstruction. It can penalize incorrect SR predictions to achieve better SR performance. Experimental results on 5 benchmark datasets show that our method outperforms state-of-theart methods in terms of both subjective visual quality and objective quantitative metrics.

Super-resolution for the extracted region of fruit image

“…In order to obtain clearer regions of interest, this paper performs Super Resolution (SR) [4] reconstruction on the extracted regions of interest. This technology starts from a software perspective and reconstructs low resolution (LR) images through relevant algorithms.…”