Utilizing Two-Phase Processing With FBLS for Single Image Deraining

Most learning-based low-light image enhancement methods typically suffer from two problems. First, they require a large amount of paired data for training, which are difficult to acquire in most cases. Second, in the process of enhancement, image noise is difficult to be removed and may even be amplified. In other words, performing denoising and illumination enhancement at the same time is difficult. As an alternative to supervised learning strategies that use a large amount of paired data, as presented in previous work, this paper presents an mixed-attention guided generative adversarial network called MAGAN for low-light image enhancement in a fully unsupervised fashion. We introduce a mixed-attention module layer, which can model the relationship between each pixel and feature of the image. In this way, our network can enhance a low-light image and remove its noise simultaneously. In addition, we conduct extensive experiments on paired and no-reference datasets to show the superiority of our method in enhancing low-light images.

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“…Similar to Ref. [42], Ref. [28] built a decoupled network with two stages to implement illumination enhancement and denoising separately.…”

Section: Deep Learning-based Low-light Image Enhancement Methodsmentioning

confidence: 99%

MAGAN: Unsupervised low-light image enhancement guided by mixed-attention

Wang

Jiang

Yang

et al. 2022

Big Data Min. Anal.

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“…2) Hand-crafted Neural Network-based De-raining Methods: The major development of deep-learning-based deraining methods lies on the manual design of various neural networks [6], [24], [31]- [33], [38], [41], [43], [45], [45]- [47], [53]- [57]. In these de-raining networks, multi-scale architecture [6], [24], [26], [31], [32], [38], [41], [55], [60] and attention module [46], [54], [61] or their combination [33], [45], [56], [57] have been widely incorporated into the design of various image de-raining networks and have achieved the promising performance.…”

Section: A Image De-raining Methodsmentioning

confidence: 99%

“…Over the past decade, a variety of successful algorithms, ranging from early hand-crafted prior-based methods [19]- [21], [27], [28] to the latest deep learning-based methods [6], [22]- [24], [26], [29]- [39], [41]- [47], [53]- [55], have been proposed to handle the image de-raining task. The priorbased de-raining methods first leverage effective regularizers to characterize the property of the background and rain streak layers, and then separate them by solving an objective function with proper optimization algorithms.…”

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confidence: 99%

Multi-scale Attentive Image De-raining Networks via Neural Architecture Search

Liu¹,

Fu²,

Wanliang³

et al. 2022

Preprint

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Multi-scale architectures and attention modules have shown effectiveness in many deep learning-based image de-raining methods. However, manually designing and integrating these two components into a neural network requires a bulk of labor and extensive expertise. In this article, a high-performance multi-scale attentive neural architecture search (MANAS) framework is technically developed for image deraining. The proposed method formulates a new multi-scale attention search space with multiple flexible modules that are favorite to the image de-raining task. Under the search space, multi-scale attentive cells are built, which are further used to construct a powerful image de-raining network. The internal multiscale attentive architecture of the de-raining network is searched automatically through a gradient-based search algorithm, which avoids the daunting procedure of the manual design to some extent. Moreover, in order to obtain a robust image de-raining model, a practical and effective multi-to-one training strategy is also presented to allow the de-raining network to get sufficient background information from multiple rainy images with the same background scene, and meanwhile, multiple loss functions including external loss, internal loss, architecture regularization loss, and model complexity loss are jointly optimized to achieve robust de-raining performance and controllable model complexity. Extensive experimental results on both synthetic and realistic rainy images, as well as the down-stream vision applications (i.e., objection detection and segmentation) consistently demonstrate the superiority of our proposed method.Index Terms-Image de-raining, multi-scale attentive neural architecture search, multi-to-one training strategy. I. INTRODUCTIONW HEN people take photos in an outdoor environment, the captured images could suffer from visibility degradation owning to various bad weather conditions such as rain [1], [2], haze [3], [4], or snow [5]. Particularly, on a rainy day, the presence of rain can seriously degrade the visual quality of outdoor images, and as a result affects the visual authenticity of human perception and the performance

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“…The methodologies have been designed for both cases. In recent years, various methods have been proposed for rain-streaks removal for both cases: video and single image as the video sequence is a combination of sequential frames (i.e., images) [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. This paper will concentrate on video-based rain-streaks removal.…”

Section: Introductionmentioning

confidence: 99%

Video Rain-Streaks Removal by Combining Data-Driven and Feature-Based Models

Islam

Paul

2021

Sensors

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Video analytics and computer vision applications face challenges when using video sequences with low visibility. The visibility of a video sequence is degraded when the sequence is affected by atmospheric interference like rain. Many approaches have been proposed to remove rain streaks from video sequences. Some approaches are based on physical features, and some are based on data-driven (i.e., deep-learning) models. Although the physical features-based approaches have better rain interpretability, the challenges are extracting the appropriate features and fusing them for meaningful rain removal, as the rain streaks and moving objects have dynamic physical characteristics and are difficult to distinguish. Additionally, the outcome of the data-driven models mostly depends on variations relating to the training dataset. It is difficult to include datasets with all possible variations in model training. This paper addresses both issues and proposes a novel hybrid technique where we extract novel physical features and data-driven features and then combine them to create an effective rain-streak removal strategy. The performance of the proposed algorithm has been tested in comparison to several relevant and contemporary methods using benchmark datasets. The experimental result shows that the proposed method outperforms the other methods in terms of subjective, objective, and object detection comparisons for both synthetic and real rain scenarios by removing rain streaks and retaining the moving objects more effectively.

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Utilizing Two-Phase Processing With FBLS for Single Image Deraining

Cited by 21 publications

References 54 publications

MAGAN: Unsupervised low-light image enhancement guided by mixed-attention

MAGAN: Unsupervised low-light image enhancement guided by mixed-attention

Multi-scale Attentive Image De-raining Networks via Neural Architecture Search

Video Rain-Streaks Removal by Combining Data-Driven and Feature-Based Models

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