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New network enhances low-light images with adaptive refinement

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CueDR-Net, a novel dual-path network designed to enhance images taken in low-light conditions. This network addresses issues like color deviation and blurred details by combining brightness adjustment with local content reconstruction. It utilizes bidirectional cue interaction and an adaptive multi-scale response refinement convolution (AMRRConv) to improve feature extraction and prediction accuracy. Evaluations on benchmark datasets show competitive performance for image enhancement and low-light detection. AI

IMPACT This research introduces a novel approach to low-light image enhancement, potentially improving visual data quality in challenging conditions for various applications.

RANK_REASON The cluster contains a research paper detailing a new model for image enhancement. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New network enhances low-light images with adaptive refinement

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The cluster contains a research paper detailing a new model for image enhancement. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yicui Shi, Yuhan Chen, Xiangfei Huang, Zhenguo Wang, Wenxuan Yu, Ying Fang, Jie Li, Guofa Li ·

    Cue-guided dual-path reconstruction with adaptive multi-scale response refinement for low-light image enhancement

    arXiv:2603.16482v2 Announce Type: replace-cross Abstract: Low-light image enhancement (LLIE) supports optical sensing and scene observation when limited photon capture and sensor noise degrade image quality. However, existing methods can introduce colour deviations or blur local …

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