Enhancement of noisy low-light images via structure-texture-noise decomposition

Jaemoon Lim, Minhyeok Heo, Chulwoo Lee, Chang-Su Kim
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引用次数: 3

Abstract

We propose a novel noisy low-light image enhancement algorithm via structure-texture-noise (STN) decomposition. We split an input image into structure, texture, and noise components, and enhance the structure and texture components separately. Specifically, we first enhance the contrast of the structure image, by extending a 2D histogram-based image enhancement scheme based on the characteristics of low-light images. Then, we reconstruct the texture image by retrieving texture components from the noise image, and enhance it by exploiting the perceptual response of the human visual system. Experimental results demonstrate that the proposed STN algorithm sharpens the texture and enhances the contrast more effectively than conventional algorithms, while removing noise without artifacts.
基于结构-纹理-噪声分解的低光噪声图像增强
提出了一种基于结构-纹理-噪声(STN)分解的噪声弱光图像增强算法。我们将输入图像分割成结构、纹理和噪声三个分量,并分别增强结构和纹理分量。具体来说,我们首先基于弱光图像的特点,扩展了基于二维直方图的图像增强方案,增强了结构图像的对比度。然后,我们从噪声图像中提取纹理分量来重建纹理图像,并利用人类视觉系统的感知响应来增强纹理图像。实验结果表明,与传统算法相比,STN算法能更有效地锐化纹理和增强对比度,同时去除噪声而不产生伪影。
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