Log In Sign Up

Gated Multiple Feedback Network for Image Super-Resolution

by   Qilei Li, et al.

The rapid development of deep learning (DL) has driven single image super-resolution (SR) into a new era. However, in most existing DL based image SR networks, the information flows are solely feedforward, and the high-level features cannot be fully explored. In this paper, we propose the gated multiple feedback network (GMFN) for accurate image SR, in which the representation of low-level features are efficiently enriched by rerouting multiple high-level features. We cascade multiple residual dense blocks (RDBs) and recurrently unfolds them across time. The multiple feedback connections between two adjacent time steps in the proposed GMFN exploits multiple high-level features captured under large receptive fields to refine the low-level features lacking enough contextual information. The elaborately designed gated feedback module (GFM) efficiently selects and further enhances useful information from multiple rerouted high-level features, and then refine the low-level features with the enhanced high-level information. Extensive experiments demonstrate the superiority of our proposed GMFN against state-of-the-art SR methods in terms of both quantitative metrics and visual quality. Code is available at


page 3

page 4

page 6

page 7

page 8

page 11

page 12

page 13


Feedback Network for Image Super-Resolution

Recent advances in image super-resolution (SR) explored the power of dee...

Feedback Pyramid Attention Networks for Single Image Super-Resolution

Recently, convolutional neural network (CNN) based image super-resolutio...

A Two-Stage Attentive Network for Single Image Super-Resolution

Recently, deep convolutional neural networks (CNNs) have been widely exp...

An Attention-Based Approach for Single Image Super Resolution

The main challenge of single image super resolution (SISR) is the recove...

MT-ORL: Multi-Task Occlusion Relationship Learning

Retrieving occlusion relation among objects in a single image is challen...

FBNet: Feedback Network for Point Cloud Completion

The rapid development of point cloud learning has driven point cloud com...

Reflash Dropout in Image Super-Resolution

Dropout is designed to relieve the overfitting problem in high-level vis...

Code Repositories


PyTorch code for our paper "Gated Multiple Feedback Network for Image Super-Resolution" (BMVC2019)

view repo