Transformed Low-Rank Model for Line Pattern Noise Removal

Chang, Yi; Yan, Luxin; Zhong, Sheng

doi:10.1109/iccv.2017.191

Cited by 137 publications

(79 citation statements)

References 34 publications

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“…In [7], GMM works as a prior to decompose a rain image into background and rain streaks layer. Chang et al [6] leverage the low-rank property of rain streaks to separate two layers. Zhu et al [24] combine three different kinds of image priors.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining

Lin

et al. 2018

Lecture Notes in Computer Science

688

599

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Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining. As contextual information is very important for rain removal, we first adopt the dilated convolutional neural network to acquire large receptive field. To better fit the rain removal task, we also modify the network. In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers. We assign different alpha-values to various rain streak layers according to the intensity and transparency by incorporating the squeeze-and-excitation block. Since rain streak layers overlap with each other, it is not easy to remove the rain in one stage. So we further decompose the rain removal into multiple stages. Recurrent neural network is incorporated to preserve the useful information in previous stages and benefit the rain removal in later stages. We conduct extensive experiments on both synthetic and real-world datasets. Our proposed method outperforms the state-of-the-art approaches under all evaluation metrics. Codes and supplementary material are available at our project webpage: https://xialipku.github.io/RESCAN.

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Section: Related Workmentioning

confidence: 99%

Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining

Lin

et al. 2018

Lecture Notes in Computer Science

688

599

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“…Li et al [26] propose to use Gaussian mixture models to model rain streaks and background separately for rain removal. Chang et al [5] propose to first affine transform the rain image into a space where rain streaks have vertical appearances and then utilize the low-rank property to remove rain streaks. Zhu et al [47] exploit rain streak directions to first determine the rain-dominant regions, which are used to guide the process of separating rain streaks from background details based on rain-dominant patch statistics.…”

Section: Related Workmentioning

confidence: 99%

Spatial Attentive Single-Image Deraining With a High Quality Real Rain Dataset

Wang

Yang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

504

418

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Removing rain streaks from a single image has been drawing considerable attention as rain streaks can severely degrade the image quality and affect the performance of existing outdoor vision tasks. While recent CNN-based derainers have reported promising performances, deraining remains an open problem for two reasons. First, existing synthesized rain datasets have only limited realism, in terms of modeling real rain characteristics such as rain shape, direction and intensity. Second, there are no public benchmarks for quantitative comparisons on real rain images, which makes the current evaluation less objective. The core challenge is that real world rain/clean image pairs cannot be captured at the same time. In this paper, we address the single image rain removal problem in two ways. First, we propose a semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images. Using this method, we construct a large-scale dataset of ∼29.5K rain/rain-free image pairs that covers a wide range of natural rain scenes. Second, to better cover the stochastic distribution of real rain streaks, we propose a novel SPatial Attentive Network (SPANet) to remove rain streaks in a local-to-global manner. Extensive experiments demonstrate that our network performs favorably against the state-of-the-art deraining methods.

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“…To address it, many models and priors are used to perform signal separation and texture classification. These models include sparse coding [112], generalized low rank model [10], nonlocal mean filter [113], discriminative sparse coding [11], Gaussian mixture model [12], rain direction prior [13], transformed low rank model [14]. The presence of deep learning has promoted the development of single image deraining.…”

Section: B Poor Visibility Enhancementmentioning

confidence: 99%

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

Yang

Yuan

Ren

et al. 2020

IEEE Trans. on Image Process.

189

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Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG 2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.Index Terms-Poor visibility environment, object detection, face detection, haze, rain, low-light conditions *The first two authors Wenhan Yang and Ye Yuan contributed equally. Ye Yuan and Wenhan Yang helped prepare the dataset proposed for the UG2+ Challenges, and were the main responsible members for UG2+ Challenge 2019 (Track 2) platform setup and technical support. Wenqi Ren, Jiaying Liu, Walter J. Scheirer, and Zhangyang Wang were the main organizers of the challenge and helped prepare the dataset, raise sponsors, set up evaluation environment, and improve the technical submission. Other authors are the group members of winner teams in UG2+ challenge Track 2 contributing to the winning methods.

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Transformed Low-Rank Model for Line Pattern Noise Removal

Cited by 137 publications

References 34 publications

Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining

Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining

Spatial Attentive Single-Image Deraining With a High Quality Real Rain Dataset

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

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