Paper Info
Title
Modulating Image Restoration With Continual Levels Via Adaptive Feature Modification Layers
Abstract
In image restoration tasks, like denoising and super-resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of little additional parameters than a single-level model yet could handle arbitrary restoration levels between a start and an end level. The additional module, namely AdaFM layer, performs channel-wise feature modification, and can adapt a model to another restoration level with high accuracy. By simply tweaking an interpolation coefficient, the intermediate model - AdaFM-Net could generate smooth and continuous restoration effects without artifacts. Extensive experiments on three image restoration tasks demonstrate the effectiveness of both model training and modulation testing. Besides, we carefully investigate the properties of AdaFM layers, providing a detailed guidance on the usage of the proposed method.
Year
DOI
Venue
2019
10.1109/CVPR.2019.01131
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
Volume
Noise reduction,Pattern recognition,Computer science,Interpolation,Tweaking,Modulation,Artificial intelligence,Deep learning,Image restoration,Superresolution
Journal
abs/1904.08118
ISSN
Citations 
PageRank 
1063-6919
2
0.37
References 
Authors
0
3
Name
Order
Citations
PageRank
Jingwen He161.83
Chao Dong2206480.72
Yu Qiao32267152.01