基于l1/2范数正则化的图像重建方法

为了利用l1]范数保持图像边缘信息的优势，并兼顾l2]范数对图像平坦区域噪声抑制的特性，提出了一种自适应范数混合模型--l12]范数正则化方法。相比于经典的l1]范数正则化方法，该方法能够得到更加稀疏的解，同时相比于传统去噪方法，该方法对自然图像的长尾分布噪声具有比较理想的去除效果。还针对范数混合模型中噪声的分布的自适应变化，设计了一种自适应收敛准则迭代方法，该方法可以有效地减少迭代次数。实验结果和分析验证了混合模型在图像重建效果和计算效率方面的有效性。

Image reconstruction method based on l_(1/2) norm regularization

The purpose of keeping image edge preservation by l1]norm, while suppressing noise by l1]norm in the field of smooth, an adaptive norm mix model--l12]norm regularization method is proposed. Compared with traditional l1]norm regularization method, this method can get much sparse solution and has an ideal effect on removing the heavy-tail distribution noise. In addition, due to noise distribution will adaptively change, an adaptive convergence rule is proposed, which can effectively decrease the iteration numbers. Experimental results and analysis show that the proposed method can get the ideal restoration result and ?computational efficiency effectively.