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UltraFast-LiNET: Lightweight network for real-time low-light image enhancement

By PulseAugur Editorial · [1 sources] ·

Researchers have developed UltraFast-LiNET, a highly efficient convolutional neural network designed for real-time low-light image enhancement on resource-constrained edge devices. The network features a novel Dynamic Shift Convolution (DSConv) operation and a Multi-Scale Shift Residual Block (MSRB) to achieve a large receptive field with minimal parameters. To address training challenges in lightweight models, a multi-level gradient-aware loss function was introduced. UltraFast-LiNET demonstrates competitive performance, achieving a PSNR of 19.81 dB on the LOL dataset with as few as 180 learnable parameters, balancing real-time processing with image quality for edge deployment. AI

IMPACT This research offers a new lightweight model for real-time image enhancement on edge devices, potentially improving performance in applications like autonomous driving and mobile photography.

RANK_REASON This is a research paper detailing a new model and methodology for image enhancement. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

UltraFast-LiNET: Lightweight network for real-time low-light image enhancement

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This is a research paper detailing a new model and methodology for image enhancement. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yuhan Chen, Yicui Shi, Guofa Li, Guangrui Bai, Wenxuan Yu, Ying Fang, Wenbo Chu, Keqiang Li ·

    UltraFast-LiNET: Light-weight multi-scale shift convolutional network for real-time low-light image enhancement

    arXiv:2512.02965v2 Announce Type: replace Abstract: Addressing the urgent need for high-performance real-time low-light image enhancement on resource-constrained edge devices in low-illumination scenarios such as nighttime and tunnels, this paper presents UltraFast-LiNET, an ultr…

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