Low-Light Image Enhancement via Adaptive Shape and Texture Prior
Low-Light Image Enhancement via Adaptive Shape and Texture Prior
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DOI：
10.1109/sitis.2019.00024
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Kazuki Kurihara;Hiromi Yoshida;Y. Iiguni
中科院分区：
文献类型：
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作者：
Kazuki Kurihara;Hiromi Yoshida;Y. Iiguni
低光图像由于其低可见度和隐藏在黑暗区域中的大量噪声而影响各种计算机视觉算法。虽然基于Retinex理论的许多方法，将观察到的图像分解成反射率和照明，已经被提出来缓解这个问题，现有的方法不可避免地导致不足和过度增强。在本文中，我们提出了一个新的联合优化方程，充分考虑了反射率和光照的特点。具体地说，我们采用L2-Lp范数正则化项来估计反射率以尽可能地保留细节和纹理，采用光照估计以尽可能地保留结构信息而不含纹理，并采用交替最小化方法求解优化方程。此外，我们引入了一个新的自适应纹理之前，揭示更多的细节和纹理与降噪的明亮和黑暗的地区。实验结果，包括定性和定量的评价，表明该方法可以建立一个更好的性能比其他国家的最先进的方法。
Low light images affect various computer vision algorithms due to their low visibility and much noise hidden in dark regions. Although many methods based on the Retinex theory, which decomposes an observed image into the reflectance and illumination, have been proposed to alleviate the problem, existing methods inevitably cause under-and over-enhancement. In this paper, we propose a new joint optimization equation that sufficiently considers the features of both reflectance and illumination. More concretely, we adopt L2-Lp norm regularization terms to estimate the reflectance as much as possible to preserve details and textures, and the illumination as much as possible to preserve the structure information with texture-less. We solve the optimization equation in an alternating minimization method. Furthermore, we introduce a new adaptive texture prior to reveal more details and textures with noise reduction on both bright and dark regions. Experimental results, including qualitative and quantitative evaluations, show that the proposed method can establish a better performance than the other state-of-the-art methods.