paper-with-me

홈 › Papers

Nighttime Visibility Enhancement by Increasing the Dynamic Range and Suppression of Light Effects

2021-06-19 · CVPR 2021 1 · Aashish Sharma, Robby T. Tan

Most existing nighttime visibility enhancement methods focus on low light. Night images, however, do not only suffer from low light, but also from man-made light effects such as glow, glare, floodlight, etc. Hence, when the existing nighttime visibility enhancement methods are applied to these images, they intensify the effects, degrading the visibility even further. High dynamic range (HDR) imaging methods can address the low light and over-exposed regions, however they cannot remove the light effects, and thus cannot enhance the visibility in the affected regions. In this paper, given a single nighttime image as input, our goal is to enhance its visibility by increasing the dynamic range of the intensity, and thus can boost the intensity of the low light regions, and at the same time, suppress the light effects (glow, glare) simultaneously. First, we use a network to estimate the camera response function (CRF) from the input image to linearise the image. Second, we decompose the linearised image into low-frequency (LF) and high-frequency (HF) feature maps that are processed separately through two networks for light effects suppression and noise removal respectively. Third, we use a network to increase the dynamic range of the processed LF feature maps, which are then combined with the processed HF feature maps to generate the final output that has increased dynamic range and suppressed light effects. Our experiments show the effectiveness of our method in comparison with the state-of-the-art nighttime visibility enhancement methods.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

For Overall Nighttime Visibility: Integrate Irregular Glow Removal With Glow-Aware Enhancement

2024-09-23 · IEEE Transactions on Circuits and Systems for Video Technology 2024 9 · Wanyu Wu, Wei Wang, Zheng Wang, Kui Jiang 외

Current low-light image enhancement (LLIE) techniques truly enhance luminance but have limited exploration on another harmful factor of nighttime visibility, the glow effects with multiple shapes in the real world. The p…

Flare RemovalImage EnhancementLow-Light Image EnhancementZero-Shot Learning

Shadow Erosion and Nighttime Adaptability for Camera-Based Automated Driving Applications

2025-04-11 · Mohamed Sabry, Gregory Schroeder, Joshua Varughese, Cristina Olaverri-Monreal

Enhancement of images from RGB cameras is of particular interest due to its wide range of ever-increasing applications such as medical imaging, satellite imaging, automated driving, etc. In autonomous driving, various te…

Autonomous Driving

Nighttime Hazy Image Enhancement via Progressively and Mutually Reinforcing Night-Haze Priors

2026-01-05 · Chen Zhu, Huiwen Zhang, Mu He, Yujie Li 외 arxiv

Enhancing the visibility of nighttime hazy images is challenging due to the complex degradation distributions. Existing methods mainly address a single type of degradation (e.g., haze or low-light) at a time, ignoring th…

Image EnhancementImage Restoration

From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement

2023-07-31 · Wanyu Wu, Wei Wang, Zheng Wang, Kui Jiang 외

Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images. However, these methods have limited explora…

Flare RemovalImage EnhancementLow-Light Image EnhancementZero-Shot Learning

Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark

2021-08-09 · ICCV 2021 10 · Kun Wang, Zhenyu Zhang, Zhiqiang Yan, Xiang Li 외

Monocular depth estimation aims at predicting depth from a single image or video. Recently, self-supervised methods draw much attention since they are free of depth annotations and achieve impressive performance on sever…

Depth EstimationImage EnhancementMonocular Depth Estimation