Low-light Image Enhancement via a Frequency-based Model with Structure and Texture Decomposition
Mingliang Zhou, Hongyue Leng, Bin Fang, Tao Xiang, Xuekai Wei, Weijia Jia
Abstract
This article proposes a frequency-based structure and texture decomposition model in a Retinex-based framework for low-light image enhancement and noise suppression. First, we utilize the total variation-based noise estimation to decompose the observed image into low-frequency and high-frequency components. Second, we use a Gaussian kernel for noise suppression in the high-frequency layer. Third, we propose a frequency-based structure and texture decomposition method to achieve low-light enhancement. We extract texture and structure priors by using the high-frequency layer and a low-frequency layer, respectively. We present an optimization problem and solve it with the augmented Lagrange multiplier to generate a balance between structure and texture in the reflectance map. Our experimental results reveal that the proposed method can achieve superior performance in naturalness preservation and detail retention compared with state-of-the-art algorithms for low-light image enhancement. Our code is available on the following website. 1