基于低秩矩阵估计的暗光图像增强模型

doi:10.11896/jsjkx.210600090

Abstract

Abstract: Due to the influence of low lightness,the images acquired at dim or backlight conditions tend to have poor visual quality.Retinex-based low-light enhancement models are effective in improving the scene lightness,but they are often limited in hand-ling the over-boosted image noise hidden in dark regions.To solve this issue,we propose a Retinex-based low-light enhancement model incorporating the low-rank matrix approximation.First,the input image is decomposed into an illumination layer I and a reflectance layer R according to the Retinex assumption.During this process,the image noise in R is suppressed via low-rank-based approximation.Then,aiming to preserve the image details in the bright regions and suppress the noise in the dark regions simultaneously,a post-fusion under the guidance of I is introduced.In experiments,qualitative and quantitative comparisons with other low-light enhancement models demonstrate the effectiveness of our method.

Key words: Fusion, Low rank matrix approximation, Low-light image, Retinex model

CLC Number:

TP391

WANG Yi-han, HAO Shi-jie, HAN Xu, HONG Ri-chang. Low-light Image Enhancement Model with Low Rank Approximation[J].Computer Science, 2022, 49(1): 187-193.

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Low-light Image Enhancement Model with Low Rank Approximation

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