Underwater image colour constancy based on DSNMF
Underwater image colour constancy based on DSNMF
复制标题
基于DSNMF的水下图像色彩恒常性
DOI：
10.1049/iet-ipr.2016.0543
复制
发表时间：
2017
影响因子：
2.3
通讯作者：
Dong Junyu
中科院分区：
文献类型：
--
作者：
Liu Xiaopeng;Zhong Guoqiang;Liu Cong;Dong Junyu
不同波长的光在水下环境中可能会发生变化，导致图像发生变化。例如，漂浮颗粒的存在会导致水下图像呈现蓝色和模糊。在这项研究中，作者提出了一种称为深度稀疏非负矩阵分解（DSNMF）的方法来估计水下图像的照明。该方法将待观测图像分割成若干块，并将每个块的通道重构为一个[ R，G，B ]矩阵。DSNMF方法将每个输入矩阵深入分解为具有稀疏约束的多层。分解矩阵的最后一层用作贴片的照明。稀疏约束调整最终图像的外观。在因式分解之后，将估计的照明应用于原始图像的每个块以获得最终图像。与现有的无参考图像质量评估的水下图像增强方法相比，该方法不仅在视觉效果和IQA方面优于现有技术，而且实现简单。
Different wavelengths of light may undergo changes in underwater environment resulting in altered images. For example, the presence of floating particles causes underwater images to appear bluish and blurred. In this study, the authors propose a method called the deep sparse non-negative matrix factorisation (DSNMF) to estimate the illumination of an underwater image. The image under observation is divided into patches and each channel of a single patch is reshaped as an [ R, G, B ] matrix. The DSNMF method deeply factorises each input matrix into multiple layers with a sparseness constraint. The last layer of the factorised matrix is used as the illumination of the patch. The sparseness constraint adjusts the appearance of the final image. After factorisation, the estimated illumination is applied to each patch of the original image to obtain the final image. Compared with state-of-the-art underwater image enhancement methods using no reference image quality assessment, not only does the proposed method outperforms current techniques in terms of its visual effect and IQA, but is also simpler to implement.