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20172022
most citedRetinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement

60 citations · 205 across the 15 of their papers we have counts for

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15 papers · 1 filter

cs.CV202238 cited

Semantic-aware Texture-Structure Feature Collaboration for Underwater Image Enhancement

Di Wang, Long Ma, Risheng Liu +1

Underwater image enhancement has become an attractive topic as a significant technology in marine engineering and aquatic robotics. However, the limited number of datasets and impe…

cs.CV20222 cited

Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration

Di Wang, Jinyuan Liu, Xin Fan +1

Recent learning-based image fusion methods have marked numerous progress in pre-registered multi-modality data, but suffered serious ghosts dealing with misaligned multi-modality d…

cs.CV202231 cited

Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection

Jinyuan Liu, Xin Fan, Zhanbo Huang +4

This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Aiming at generating an image of high visual quality, previous ap…

cs.CV202060 cited

Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement

Risheng Liu, Long Ma, Jiaao Zhang +2

Low-light image enhancement plays very important roles in low-level vision field. Recent works have built a large variety of deep learning models to address this task. However, the…

cs.CV2019

Investigating Task-driven Latent Feasibility for Nonconvex Image Modeling

Risheng Liu, Pan Mu, Jian Chen +2

Properly modeling latent image distributions plays an important role in a variety of image-related vision problems. Most exiting approaches aim to formulate this problem as optimiz…

cs.CV2019

Semi-supervised Skin Detection by Network with Mutual Guidance

Yi He, Jiayuan Shi, Chuan Wang +5

In this paper we present a new data-driven method for robust skin detection from a single human portrait image. Unlike previous methods, we incorporate human body as a weak semanti…