基于倒谱与频谱分析的模糊核估计算法

图像盲去模糊是典型的图像和信号处理问题,其目的是从模糊图像中恢复出模糊核及清晰图像。在模糊核估计方面,以往的算法通常将模糊核尺度作为必要的输入参数,近年来有些算法虽然能较准确的估计参数化模糊核,但不能有效估计自然模糊图像中普遍存在的非参数化模糊核。文中利用图像梯度倒谱估计模糊核后再利用频谱分析以进一步精确的估计小尺寸模糊核的尺度。实验结果表明,文中提出的方法能适用于绝大多数场景下自然模糊图像的模糊核尺度估计。

Blur Kernel Estimation Algorithm Based on Cepstrum and Spectrum Analysis

Blind image deblurring is a classical image and signal processing problem, which aims to recover a blur kernel and a clear latent image from a blurry image. In the case of fuzzy kernel estimation, the size of blur kernel in earlier algorithms is an input parameter. In recent years, some algorithms can accurately estimate parameterized blur kernel. The none-parameterized blur kernel is more usual in the natural environment, while existing algorithms can not accurately deal with none-parameterized blur kernel. In this paper, cepstrum of image gradient was used to estimate the blur kernel and spectrum was used to further estimate the blur kernel with small size. Experiments results demonstrated that the proposed method could be applied to the fuzzy kernel-scale estimation of natural blurred images in most scenes.