Decoupling Degradation and Content Processing for Adverse Weather Image Restoration

• URL: http://arxiv.org/abs/2312.05006v1
• Date: Fri, 8 Dec 2023 12:26:38 GMT
• Title: Decoupling Degradation and Content Processing for Adverse Weather Image Restoration
• Authors: Xi Wang, Xueyang Fu, Peng-Tao Jiang, Jie Huang, Mi Zhou, Bo Li, Zheng-Jun Zha
• Abstract summary: Adverse weather image restoration strives to recover clear images from those affected by various weather types, such as rain, haze, and snow. Previous techniques can handle multiple weather types within a single network, but they neglect the crucial distinction between these two processes, limiting the quality of restored images. This work introduces a novel adverse weather image restoration method, called DDCNet, which decouples the degradation removal and content reconstruction process at the feature level based on their channel statistics.
• Score: 79.59228846484415
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Adverse weather image restoration strives to recover clear images from those affected by various weather types, such as rain, haze, and snow. Each weather type calls for a tailored degradation removal approach due to its unique impact on images. Conversely, content reconstruction can employ a uniform approach, as the underlying image content remains consistent. Although previous techniques can handle multiple weather types within a single network, they neglect the crucial distinction between these two processes, limiting the quality of restored images. This work introduces a novel adverse weather image restoration method, called DDCNet, which decouples the degradation removal and content reconstruction process at the feature level based on their channel statistics. Specifically, we exploit the unique advantages of the Fourier transform in both these two processes: (1) the degradation information is mainly located in the amplitude component of the Fourier domain, and (2) the Fourier domain contains global information. The former facilitates channel-dependent degradation removal operation, allowing the network to tailor responses to various adverse weather types; the latter, by integrating Fourier's global properties into channel-independent content features, enhances network capacity for consistent global content reconstruction. We further augment the degradation removal process with a degradation mapping loss function. Extensive experiments demonstrate our method achieves state-of-the-art performance in multiple adverse weather removal benchmarks.

Related papers

• Learning Frequency-Aware Dynamic Transformers for All-In-One Image Restoration [15.561713188178455]
  We propose a new all-in-one image restoration approach from a frequency perspective, leveraging advanced vision transformers. Our method consists of two main components: a frequency-aware Degradation prior learning transformer (Dformer) and a degradation-adaptive Restoration transformer (Rformer)
  arXiv  Detail & Related papers  (2024-06-30T13:14:44Z)
• AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation [99.57024606542416]
  We propose an adaptive all-in-one image restoration network based on frequency mining and modulation. Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands. The proposed model achieves adaptive reconstruction by accentuating the informative frequency subbands according to different input degradations.
  arXiv  Detail & Related papers  (2024-03-21T17:58:14Z)
• Dynamic Association Learning of Self-Attention and Convolution in Image Restoration [56.49098856632478]
  CNNs and Self attention have achieved great success in multimedia applications for dynamic association learning of self-attention and convolution in image restoration. This paper proposes an association learning method to utilize the advantages and suppress their shortcomings, so as to achieve high-quality and efficient inpainting.
  arXiv  Detail & Related papers  (2023-11-09T05:11:24Z)
• Always Clear Days: Degradation Type and Severity Aware All-In-One Adverse Weather Removal [8.58670633761819]
  All-in-one adverse weather removal is an emerging topic on image restoration, which aims to restore multiple weather degradations in an unified model. We propose a degradation type and severity aware model, called UtilityIR, for blind all-in-one bad weather image restoration.
  arXiv  Detail & Related papers  (2023-10-27T17:29:55Z)
• All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation [47.00239809958627]
  We propose a novel All-in-one Multi-degradation Image Restoration Network (AMIRNet) AMIRNet learns a degradation representation for unknown degraded images by progressively constructing a tree structure through clustering. This tree-structured representation explicitly reflects the consistency and discrepancy of various distortions, providing a specific clue for image restoration.
  arXiv  Detail & Related papers  (2023-08-06T04:51:41Z)
• Learning to Restore a Single Face Image Degraded by Atmospheric Turbulence using CNNs [93.72048616001064]
  Images captured under such condition suffer from a combination of geometric deformation and space varying blur. We present a deep learning-based solution to the problem of restoring a turbulence-degraded face image.
  arXiv  Detail & Related papers  (2020-07-16T15:25:08Z)
• Gated Fusion Network for Degraded Image Super Resolution [78.67168802945069]
  We propose a dual-branch convolutional neural network to extract base features and recovered features separately. By decomposing the feature extraction step into two task-independent streams, the dual-branch model can facilitate the training process.
  arXiv  Detail & Related papers  (2020-03-02T13:28:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.