Enhancing Local-Global Representation Learning for Image Restoration

Vision systems are the core element in industrial systems, such as intelligent transportation systems and inspection robots. However, undesired degradations caused by bad weather or low-end devices reduce the visibility of images. Image restoration aims to reconstruct a sharp image from a degraded c...

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Vydané v:IEEE transactions on industrial informatics Ročník 20; číslo 4; s. 6522 - 6530
Hlavní autori: Cui, Yuning, Knoll, Alois
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Convolutional neural networks
Feature maps
Frequency modulation
Image defocus deblurring
Image degradation
image dehazing
image desnowing
Image enhancement
image motion deblurring
Image reconstruction
Image restoration
Intelligent transportation systems
Laplace equations
Learning
local–global representation learning (LRL)
Representation learning
Representations
Task analysis
Transformers
Vision systems
ISSN:1551-3203, 1941-0050
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Popis
Shrnutí:Vision systems are the core element in industrial systems, such as intelligent transportation systems and inspection robots. However, undesired degradations caused by bad weather or low-end devices reduce the visibility of images. Image restoration aims to reconstruct a sharp image from a degraded counterpart and plays an important role in industrial systems. Recent transformer-based architectures leverage the self-attention unit and convolutions to model long-range dependencies and local connectivity, respectively, achieving promising performance for image restoration. However, these methods have quadratic complexity with respect to the input size. In addition, convolution operators are ineffective enough to recover the local details. This article presents a joint local and global representation learning framework for image restoration, called LoGoNet. Specifically, to enhance global contexts, we excavate the potential of pooling techniques to refine large-scale feature maps, which help handle large-size degradations. Furthermore, we develop a novel module to emphasize local edges with the implicit Laplace operator. With these designs, the proposed LoGoNet produces powerful feature representations for image restoration, which is helpful for perceiving objects of different sizes in industrial systems. Extensive experiments demonstrate that LoGoNet achieves state-of-the-art performance on nine datasets for four image restoration tasks: image defocus/motion deblurring, image dehazing, and image desnowing.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2023.3345464