基于结构加权低秩近似的泊松图像去模糊

Structural weighted low-rank approximation for Poisson image deblurring

针对由高斯模糊和泊松噪声引起的图像降质问题,提出了一种基于结构加权低秩近似的图像去模糊方法。首先,通过依次组合缩放、旋转、剪切和翻折等四种基本操作引入结构变换,以增加搜索空间内候选图像块的相似性。然后,构造新的目标函数,利用相似图像块的低秩性,在正则项中使用加权核范数(WNN)对结构变换后的图像块进行惩罚,以在去模糊的同时抑制泊松噪声。最后,基于半正定二次分裂(HQS)方法设计交替优化方案,用于求解目标函数,从泊松图像中去除模糊。实验结果表明:在多种泊松噪声强度下,所提方法取得的峰值信噪比(PSNR)和结构相似性(SSIM)都高于当前同类去模糊方法。

To solve the problem of image quality degradation caused by Gaussian blur and Poisson noise,an image deblurring method based on structural weighted low-rank approximation is proposed.First,a structural transformation is introduced by subsequently combining the four basic operations of scaling,rotation,shearing,and flipping in order to boost the similarity of candidate patches in the searching space.Then,a novel objective function is proposed by carefully designing the regularization term.To this end,we perform structural transformation on image patches and then penalize the transformed results with Weighted Nuclear Norm(WNN)based on the assumption of low-rank among non-local similar patches,suppressing Poisson noise at the same time of deblurring.Finally,an alternating optimization algorithm is presented based on the Half-Quadratic Splitting(HQS)method to solve the proposed objective function for Poisson image deblurring.Experimental results demonstrate that,under multiple intensities of Poisson noise,the proposed algorithm achieves higher Peak Signal to Noise Ratio(PSNR)and Structural Similarity(SSIM)than the state-of-the-art deblurring methods.