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20162023
most citedTowards Low Light Enhancement with RAW Images

76 citations · 115 across the 12 of their papers we have counts for

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10 papers · 1 filter

cs.CV20232 cited

Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger

Yi Yu, Yufei Wang, Wenhan Yang +3

Recent deep-learning-based compression methods have achieved superior performance compared with traditional approaches. However, deep learning models have proven to be vulnerable t…

cs.CV2023

Raw Image Reconstruction with Learned Compact Metadata

Yufei Wang, Yi Yu, Wenhan Yang +4

While raw images exhibit advantages over sRGB images (e.g., linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requir…

cs.CV2023

Removing Image Artifacts From Scratched Lens Protectors

Yufei Wang, Renjie Wan, Wenhan Yang +3

A protector is placed in front of the camera lens for mobile devices to avoid damage, while the protector itself can be easily scratched accidentally, especially for plastic ones.…

cs.CV2022

Meta-Interpolation: Time-Arbitrary Frame Interpolation via Dual Meta-Learning

Shixing Yu, Yiyang Ma, Wenhan Yang +2

Existing video frame interpolation methods can only interpolate the frame at a given intermediate time-step, e.g. 1/2. In this paper, we aim to explore a more generalized kind of v…

cs.CV2022

Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement

Zhangkai Ni, Wenhan Yang, Hanli Wang +3

Getting rid of the fundamental limitations in fitting to the paired training data, recent unsupervised low-light enhancement methods excel in adjusting illumination and contrast of…

cs.CV202214 cited

Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression

Yeying Jin, Wenhan Yang, Robby T. Tan

Night images suffer not only from low light, but also from uneven distributions of light. Most existing night visibility enhancement methods focus mainly on enhancing low-light reg…