光学学报, 2018, 38 (3): 0310001, 网络出版: 2018-03-20
基于稀疏特征提取的单幅图像去雾 下载: 1073次
Single Image Dehazing Based on Sparse Feature Extraction
图像处理 图像增强 图像去雾 稀疏表示 字典学习 特征提取 image processing image enhancement image dehazing sparse representation dictionary learning feature extraction
摘要
为解决暗通道先验去雾算法在天空区域和大片白色区域色彩失真的问题, 提出了一种基于稀疏表示模型和特征提取的单幅图像去雾算法。通过稀疏字典的训练过程, 学习雾天图像的稀疏特征, 初步优化粗略介质传输图的稀疏系数。根据雾天灰度图像的稀疏特征, 进一步精细化介质传输图。逆向求解雾天退化模型, 得到去雾图像。实验结果表明, 所提算法在天空区域的处理上优势明显, 同时恢复出更多的图像细节和边缘信息。
Abstract
To overcome the color distortion in sky regions and large white regions brought by the dark channel prior dehazing algorithm, we propose a single image dehazing algorithm based on sparse representation model and feature extraction. Firstly, the algorithm learns the sparse features of foggy images via training sparse dictionary, and optimizes the sparse coefficients of the rough medium transmission image preliminarily. Then, the algorithm refines the medium transmission image by the sparse features of foggy gray images. Finally, with the converse solution of the degradation model, the algorithm obtains the dehazing image. The experimental results demonstrate that the proposed algorithm has obvious advantages in the processing of the sky area, and at the same time, it can recover more image details and marginal information.
刘坤, 毕笃彦, 王世平, 何林远, 高山. 基于稀疏特征提取的单幅图像去雾[J]. 光学学报, 2018, 38(3): 0310001. Liu Kun, Bi Duyan, Wang Shiping, He Linyuan, Gao Shan. Single Image Dehazing Based on Sparse Feature Extraction[J]. Acta Optica Sinica, 2018, 38(3): 0310001.