75,039 Hits in 5.3 sec

Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement [article]

Kui Jiang, Zhongyuan Wang, Zheng Wang, Chen Chen, Peng Yi, Tao Lu, Chia-Wen Lin
2021 arXiv   pre-print
Low-light image enhancement aims to improve an image's visibility while keeping its visual naturalness.  ...  Inspired by the color image formulation (diffuse illumination color plus environment illumination color), we first estimate the degradation from low-light inputs to simulate the distortion of environment  ...  Nine representative low-light image enhancement methods are employed for comparison.  ... 
arXiv:2103.10621v3 fatcat:d3x4edxkxndehdjb2qvkm2jpju

GenISP: Neural ISP for Low-Light Machine Cognition [article]

Igor Morawski and Yu-An Chen and Yu-Sheng Lin and Shusil Dangi and Kai He and Winston H. Hsu
2022 arXiv   pre-print
Finally, we contribute a low-light dataset of 7K raw images annotated with 46K bounding boxes for task-based benchmarking of future low-light image restoration and object detection.  ...  to restore and enhance the image.  ...  Deep ISPs also have been proposed to learn joint image restoration and low-light image enhancement [2, 12, 13] . Deep ISPs for low-light image restoration.  ... 
arXiv:2205.03688v1 fatcat:v7wklzna6va2pejq6kkv7r7l4u

Semantic Segmentation of Crop and Weed using an Encoder-decoder Network and Image Enhancement Method under Uncontrolled Outdoor Illumination

Aichen Wang, Yifei Xu, Xinhua Wei, Bingbo Cui
2020 IEEE Access  
Image enhancement improved the image quality and consequently the robustness of segmentation models against different lighting conditions.  ...  Three image enhancement methods were investigated to improve model robustness against different lighting conditions.  ...  For the RGB images, it seems that the lighting condition is not well, as the brightness and contrast of the RGB images are low, as shown in Figure 1a .  ... 
doi:10.1109/access.2020.2991354 fatcat:flqxekvbeffcdjhriotoij6xkq

HLA-Face: Joint High-Low Adaptation for Low Light Face Detection [article]

Wenjing Wang, Wenhan Yang, Jiaying Liu
2021 arXiv   pre-print
To reduce the burden of building new datasets for low light conditions, we make full use of existing normal light data and explore how to adapt face detectors from normal light to low light.  ...  Therefore, most existing low-light enhancement and adaptation methods do not achieve desirable performance. To address the issue, we propose a joint High-Low Adaptation (HLA) framework.  ...  For RGB images, Loh et al. build the ExDark [28] dataset and analyze the low light images using both hand-crafted and learned features.  ... 
arXiv:2104.01984v1 fatcat:jvwmckik6bg2rbrfoqh3yd7fhy

VCIP 2020 Index

2020 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)  
Structure Loh, Yuen Peng DEN: Disentanglement and Enhancement Networks for Low Illumination Images Lou, Jian Adaptive Resolution Change for Versatile Vide Coding Lu, Guo Learned image and video  ...  Jay Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation Kuo, C.-C. Jay Noise-Aware Texture-Preserving Low-Light Enhancement Kuo, C.-C.  ... 
doi:10.1109/vcip49819.2020.9301896 fatcat:bdh7cuvstzgrbaztnahjdp5s5y

Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement [article]

Yang Wang and Yang Cao and Zheng-Jun Zha and Jing Zhang and Zhiwei Xiong and Wei Zhang and Feng Wu
2019 arXiv   pre-print
Contrast enhancement and noise removal are coupled problems for low-light image enhancement.  ...  low-light enhancement results.  ...  Thus, the IM-Net can learn better representation for illumination distribution.  ... 
arXiv:1911.11323v1 fatcat:xv76mtzvi5cjbg7ytzh7jhfgsa

Enhance Images as You Like with Unpaired Learning [article]

Xiaopeng Sun, Muxingzi Li, Tianyu He, Lubin Fan
2021 arXiv   pre-print
Low-light image enhancement exhibits an ill-posed nature, as a given image may have many enhanced versions, yet recent studies focus on building a deterministic mapping from input to an enhanced version  ...  In contrast, we propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space, given only sets of low- and  ...  Acknowledgments We thank Jing Ren for useful suggestions on the manuscript and all the anonymous reviewers for their valuable comments.  ... 
arXiv:2110.01161v1 fatcat:nju7e7emnvhk5cdny5fjvnuofq

Disentangle Perceptual Learning through Online Contrastive Learning [article]

Kangfu Mei, Yao Lu, Qiaosi Yi, Haoyu Wu, Juncheng Li, Rui Huang
2020 arXiv   pre-print
The contrastive learning aims at activating the perception-relevant dimensions and suppressing the irrelevant ones by using the triplet loss, so that the original representation can be disentangled for  ...  Under such an assumption, we try to disentangle the perception-relevant dimensions from the representation through our proposed online contrastive learning.  ...  RAW Low-light Image Enhancement In the area of low-light enhancement, RAW images have got lots of attention in recent works [3] [2] .  ... 
arXiv:2006.13511v1 fatcat:nxtbmwuso5dtjhdg3pjc4spmvm

Low-Light Image Enhancement Based on Multi-Path Interaction

Bai Zhao, Xiaolin Gong, Jian Wang, Lingchao Zhao
2021 Sensors  
In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhance the R, G, B channels, and then the three channels are combined into the color image  ...  Experimental results show that the proposed method can effectively improve the visual quality of low-light images, and the performance is better than the state-of-the-art methods.  ...  [27] considered the low-light image enhancement as a residual learning problem.  ... 
doi:10.3390/s21154986 fatcat:l7xzhe6mljhuxaftzp7wd3ei7y

Attention-based network for low-light image enhancement [article]

Cheng Zhang, Qingsen Yan, Yu zhu, Xianjun Li, Jinqiu Sun, Yanning Zhang
2020 arXiv   pre-print
The captured images under low light conditions often suffer insufficient brightness and notorious noise. Hence, low-light image enhancement is a key challenging task in computer vision.  ...  A variety of methods have been proposed for this task, but these methods often failed in an extreme low-light environment and amplified the underlying noise in the input image.  ...  Low-light Image Enhancement Image enhancement has a long history in low-level vision.  ... 
arXiv:2005.09829v2 fatcat:xmkzkzbi6be65dzvwog2boo3ca

Semantically Contrastive Learning for Low-light Image Enhancement [article]

Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, Huiyu Zhou
2021 arXiv   pre-print
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.  ...  SCL-LLE allows the LLE model to learn from unpaired positives (normal-light)/negatives (over/underexposed), and enables it to interact with the scene semantics to regularize the image enhancement network  ...  Semantically Contrastive Learning for Low-light Image Enhancement Dong Liang1 , Ling Li1 , Mingqiang  ... 
arXiv:2112.06451v1 fatcat:2iri5zu6j5gltkdd4gufq7sphq

Improving Extreme Low-Light Image Denoising via Residual Learning

Paras Maharjan, Li Li, Zhu Li, Ning Xu, Chongyang Ma, Yue Li
2019 2019 IEEE International Conference on Multimedia and Expo (ICME)  
In this paper, we propose a new residual learning based deep neural network for end-to-end extreme low-light image denoising that can not only significantly reduce the computational cost but also improve  ...  by the Dean of the School of Computing and Engineering, have examined a thesis titled "Improving Extreme Low-light Image Denoising via Residual Learning," presented by Paras Maharjan, candidate for the  ...  Low-light Image Enhancement Histogram equalization and gamma correction are the most common traditional methods for image enhancement.  ... 
doi:10.1109/icme.2019.00162 dblp:conf/icmcs/MaharjanLLXML19 fatcat:3hyiav6shrartjgfc3tgfs3drm

Learning Digital Camera Pipeline for Extreme Low-Light Imaging [article]

Syed Waqas Zamir, Aditya Arora, Salman Khan, Fahad Shahbaz Khan, Ling Shao
2019 arXiv   pre-print
improving the visual quality of these low-light images.  ...  , low-light RAW sensor data to well-exposed sRGB images.  ...  We then discuss the recently introduced learning-based approach specifically designed for low-light imaging.  ... 
arXiv:1904.05939v1 fatcat:asfd53sorzapzf7syr723ts5pq

Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection [article]

Ziteng Cui, Guo-Jun Qi, Lin Gu, Shaodi You, Zenghui Zhang, Tatsuya Harada
2022 arXiv   pre-print
In a self-supervision manner, the MAET learns the intrinsic visual structure by encoding and decoding the realistic illumination-degrading transformation considering the physical noise model and image  ...  Dark environment becomes a challenge for computer vision algorithms owing to insufficient photons and undesirable noise.  ...  Low-Light Vision Enhancement and Restoration Methods Low-light vision tasks focus on the human visual experience by restoring details and correcting the color shift.  ... 
arXiv:2205.03346v1 fatcat:idbmgvgj7bhkbdamkdm4zseex4

A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

Shiping Ma, Hongqiang Ma, Yuelei Xu, Shuai Li, Chao Lv, Mingming Zhu
2018 Sensors  
In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed.  ...  Then, the original low-light image is transformed from RGB to HSI color space.  ...  Evaluation on Real-Word Dataset In order to show the proposed algorithm capability for the enhancement of not only the synthetic low-light images but also for actual low-light images, 17 typical images  ... 
doi:10.3390/s18103583 fatcat:krdwxefctfafriejbnidsow4gq
« Previous Showing results 1 — 15 out of 75,039 results