激光与光电子学进展, 2016, 53 (11): 111002, 网络出版: 2016-11-14
基于字典学习的梯度重权非局部平均的强噪声图像去噪 下载: 517次
Denoising of Strong Noisy Image via Gradient Reweighted Non-Local Averaging over Learned Dictionaries
图像处理 图像去噪 字典学习 非局部平均 梯度重权法 image processing image denoising dictionary learning non-local averaging gradient reweighted method
摘要
为从强噪声图像中重构出原图像并减小误差,提出了一种基于梯度重权非局部平均的强噪声图像去噪算法。根据稀疏和冗余表示,基于K-SVD字典学习去噪算法可自适应从已知带噪图像中训练字典,但是字典固有的结构限制,导致强噪声图像去噪效果差。提出了基于字典学习的梯度重权非局部平均算法,该算法对图像结构赋予更紧约束,可以改善去噪性能。利用全变分法求解图像结构的梯度,给予图像边缘信息更高的权重,结合图像结构信息的相似性和稀疏性先验,求解优化后的逆问题。与传统字典去噪相比,所提出的算法对强噪声图像的去噪效果更好,并保留了细节轮廓信息,具备较好的峰值信噪比和结构相似性。
Abstract
In order to reconstruct the original image from strong noise image and reduce the error, an improved image denoising algorithm for strong noise images is proposed, which is addressed as gradient reweighted non-local averaging. According to the sparse and redundant representation, the approach is based on K-SVD trained dictionaries, which are learned from the corrupted image itself and lead to sparser representations. Nevertheless, the denoising quality is bad for the strong noise image because of the intrinsic structure of dictionaries. The method is proposed to find the inherent structure of images using non-local averaging algorithm with gradient reweighting, which is obtained by total variation, as the tighter constraint over the image. According to the information of edges as the image prior and the redundancy, the optimized solution is used to solve an inverse problem by defining the area of edges higher weights. Compared with the traditional dictionary denoising, the proposed algorithm not only show the superiority of the noise drawn images in peak signal to noise ratio, but also keeps the detail information in structure similarity.
余临倩, 覃亚丽, 张晓帅. 基于字典学习的梯度重权非局部平均的强噪声图像去噪[J]. 激光与光电子学进展, 2016, 53(11): 111002. Yu Linqian, Qin Yali, Zhang Xiaoshuai. Denoising of Strong Noisy Image via Gradient Reweighted Non-Local Averaging over Learned Dictionaries[J]. Laser & Optoelectronics Progress, 2016, 53(11): 111002.