基于稀疏表示与字典学习的彩色图像去噪算法

针对彩色图像在去噪时易产生模糊现象和伪色彩的问题，提出多信息结合字典算法。首先提出了基于RGB颜色空间各通道模值的加权梯度定义，并在此基础上建立了由彩色图像的亮度、加权梯度、颜色信息结合的一种过完备结构字典。其次利用噪声图像的稀疏性，通过不断更新迭代的字典训练过程，找到最优稀疏系数和最优学习字典，从而将噪声信息和图像有用信息分离开，精确重构图像并单求其颜色，进而得到去噪后的彩色图像。实验结果显示，与已有算法相比，本文提出的算法在不同的噪声强度下都取得了更好的视觉效果和更高的客观评价指标值，表明该算法具有良好的去噪性能。

A color image denoising algorithm based onsparse representation and dictionary learning

To solve the problems of fuzzy phenomenon and pseudo color in the denoising process of color images, a dictionary algorithm combining multiple information is proposed. Firstly, the definitions of the weighted gradients based on the channel model values in RGB color space are presented. On this basis, an over-complete structure of the dictionary is established, which uses brightness, weighted gradient and color information. Secondly, the iterative dictionary training process is continually updated and processed by using the sparsity of the noised image, and the optimal sparse coefficients and optimal learning dictionary are found, which can separate noise information from useful information of images, accurately reconstruct images which only requiring the color values computation and obtain the denoised color image. Experimental results show that, compared with the existing algorithms, the proposed algorithm achieves better visual effects and higher objective index values under different noise intensities, indicating that the algorithm has good denoising performance.