Spatially-Adaptive Residual Networks for Efficient Image and Video Deblurring

03/25/2019
by   Kuldeep Purohit, et al.
0

In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur. Restoration of images affected by severe blur necessitates a network design with a large receptive field, which existing networks attempt to achieve through simple increment in the number of generic convolution layers, kernel-size, or the scales at which the image is processed. However, increasing the network capacity in this manner comes at the expense of increase in model size and inference speed, and ignoring the non-uniform nature of blur. We present a new architecture composed of spatially adaptive residual learning modules that implicitly discover the spatially varying shifts responsible for non-uniform blur in the input image and learn to modulate the filters. This capability is complemented by a self-attentive module which captures non-local relationships among the intermediate features and enhances the receptive field. We then incorporate a spatiotemporal recurrent module in the design to also facilitate efficient video deblurring. Our networks can implicitly model the spatially-varying deblurring process, while dispensing with multi-scale processing and large filters entirely. Extensive qualitative and quantitative comparisons with prior art on benchmark dynamic scene deblurring datasets clearly demonstrate the superiority of the proposed networks via reduction in model-size and significant improvements in accuracy and speed, enabling almost real-time deblurring.

READ FULL TEXT

page 7

page 8

research
04/11/2020

Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring

This paper tackles the problem of motion deblurring of dynamic scenes. A...
research
01/01/2022

Adaptive Single Image Deblurring

This paper tackles the problem of dynamic scene deblurring. Although end...
research
08/19/2020

Blur-Attention: A boosting mechanism for non-uniform blurred image restoration

Dynamic scene deblurring is a challenging problem in computer vision. It...
research
01/19/2021

BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring

Image motion blur usually results from moving objects or camera shakes. ...
research
06/17/2013

Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior

Typical blur from camera shake often deviates from the standard uniform ...
research
08/19/2021

Spatially-Adaptive Image Restoration using Distortion-Guided Networks

We present a general learning-based solution for restoring images suffer...
research
12/23/2016

Blind restoration for non-uniform aerial images using non-local Retinex model and shearlet-based higher-order regularization

Aerial images are often degraded by space-varying motion blur and simult...

Please sign up or login with your details

Forgot password? Click here to reset