基于二阶梯度的暗通道先验盲图像去模糊

Dark channel a priori blind image deblurring based on second order gradient

盲图像去模糊的目的是通过迭代在模糊核未知的情况下,从模糊图像中恢复出清晰图像。当真实图像具有较少的暗像素时,暗通道先验算法不能产生令人满意的结果。实验中发现,二阶梯度(海森矩阵)中元素的绝对值随着图像逐渐模糊而减小。利用这一特点,提出一种基于L1正则化二阶梯度的暗通道先验盲图像去模糊算法模型。首先,展示了算法相关的理论证明,通过实验说明海森矩阵在保留边缘细节以及图像细节方面的可行性,其次在暗通道去模糊模型的基础上,引入二阶梯度项并施以L1范数约束。然后采用半二次分裂策略来解决该非凸优化问题,最后,使用快速傅里叶变换求得最终的清晰图像和模糊核。实验结果表明,该算法能够在抑制噪声的同时很好地保护图像的边缘细节和消除振铃伪影,并且在合成图像和自然图像上都比现有的图像去模糊方法鲁棒性更强,并且性能良好。在自然图像数据集中SSIM值平均提高了10%以上。

The purpose of blind image deblurring is to recover a clear image from a blurred image by iterating in the case where the blur kernel is unknown.When the real image has fewer dark pixels,the dark channel a priori algorithm does not produce satisfactory results.It is found that the absolute value of the elements in the second-order gradient(Hessian matrix)decreases as the image is gradually blurred.Using this feature,a model of dark channel a priori blind image deblurring algorithm based on regularized second-order gradients is proposed.Firstly,the theoretical proofs related to the algorithm are shown,and the feasibility of the Hessian matrix in preserving edge details and image details is experimentally illustrated.Then,a semi-quadratic splitting strategy is used to solve the nonconvex optimization problem,and finally,the fast Fourier transform is used to obtain the final clear image and the blurred kernel.The experimental results show that the algorithm can well preserve the edge details and eliminate ringing artifacts while suppressing noise,and it is more robust and performs well than existing image deblurring methods on both synthetic and natural images.The SSIM values are improved by more than 10%on average in the natural image dataset.