Restoration of Multiple Image Distortions using a Semi-dynamic Deep Neural Network
Pages 7871 - 7880
Abstract
Restoring multiple image distortions with a single model is difficult because different distortions require fundamentally different processing mechanisms, e.g., deblurring requires high-pass filtering, while denoising requires low-pass filtering operations. This paper presents a dynamic universal image restoration (DUIR) system capable of simultaneously processing multiple distortions. The new model features several innovative designs: (i) a distortion embedding module (DEM) to automatically encode the distortion information of an input, (ii) a distortion attention module (DAM) that uses a bi-directional long short-term memory (LSTM) to encode the distortion into a sequence of forward and backward interdependent modulating signals, and (iii) a dynamically adaptive image restoration deep convolutional neural network (DAIR-DCNN) featuring unique semi-dynamic layers (SDLs) in which part of their parameters are dynamically modulated by the distortion signals. DEM, DAM, and SDLs together make DAIR-DCNN adaptive to the distortions of the current input, which in turn equips the DUIR system with the capability of simultaneously processing multiple image distortions with a single trained model. We present extensive experimental results to show that the new technique achieves superior performance to state-of-the-art models on both synthetic and real data. We further demonstrate that a trained DUIR system can simultaneously handle different distortions, including those with conflicting demands, such as denoising, deblurring, and compression artifact removal.
Supplemental Material
MP4 File
In this video, we will talk about our work "Restoration of Multiple Image Distortions using a Semi-dynamic Deep Neural Network". The motivation, data observation and model analysis are introduced in the first part. Then, we will talk about our model construction and how to train this model. The final part showed a variety of results of experiments including model comparison in datasets and real-world datasets, our ablation study, as well as many visualisation results.
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- Restoration of Multiple Image Distortions using a Semi-dynamic Deep Neural Network
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Published In
October 2023
9913 pages
ISBN:9798400701085
DOI:10.1145/3581783
- General Chairs:
- Abdulmotaleb El Saddik,
- Tao Mei,
- Rita Cucchiara,
- Program Chairs:
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- Diana Patricia Tobon Vallejo,
- Pradeep K. Atrey,
- M. Shamim Hossain
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Published: 27 October 2023
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- Guangdong Basic and Applied Basic Research Foundation
- the National Natural Science Foundation of China
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