This technique is obtained by fusing zero-reference deep curve estimation (Zero-DCE) and dark channel prior (DCP). We calculate image-specific parameter curves using convolutional neural networks (CNNs), which enhance the low-light image pixel-wise. The proposed method follows a spatial attention...
Zero-DCE方法:定义无参考损失函数Non-Reference Loss Functions,不需要任何paired或unpaired的数据 二、算法实现 Zero−DCE包括如下三个部分: Light-Enhancement curves (LE-curves) 光增强曲线 Deep Curve Estimation Network(DCE-Net) 深度曲线估计网络 Non-Reference Loss 无参考损失函数 [1] Light-Enhancement curve...
Zero-Reference Deep Curve Estimation (Zero-DCE) is currently one of the most popular low-light image enhancement methods. Through extensive experimentation, we observe that: (i) the excellent performance of Zero-DCE depends on the training data with multiple exposure levels, (ii) it cannot effect...
论文笔记:Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement,程序员大本营,技术文章内容聚合第一站。
Zero-Reference Deep Curve Estimation (Zero-DCE) pioneers a new idea for Low-Light Image Enhancement (LLIE), which is to formulate LLIE as a task of image-s
Zero-shot learning-based methods: ExCNet [27], Zero-DCE [28], RRDNet [29], Zero-DCE++ [30], RetinexDIP [31], and RUAS [32]. Semi-supervised learning-based method: DRBN [33] and DRBN [34]. Traditional methods for low-light enhancement include Histogram Equalization-based methods [...
论文EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network翻译和解读,程序员大本营,技术文章内容聚合第一站。
2019VISIGRAPPEnd-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional Networkspdf 2020CVPRZero-Reference Deep Curve Estimation for Low-Light Image EnhancementpdfwebcodeZero-DCE 2020CVPRLearning to Restore Low-Light Images via Decomposition-and-Enhancementpdf ...
Link to the paper:Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement Some of my thoughts and observations during my implementatino journey of this non-reference image enhancement network can be found inImplementation Details. ...
In this paper, we propose an efficient and fast low-light image enhancement method using an atmospheric scattering model based on an inverted low-light image. The transmission map is derived as a function of two saturations of the original image in the two color spaces. Due to the difficulty...