图像抖动核函数估计与图像恢复

抖动模糊是摄影中常见的问题,为此提出了一个鲁棒快速的核函数估计和图像恢复方法。给定一幅因相机抖动而模糊的图像,该方法首先建立金字塔,然后自顶向下、迭代地估计运动模糊核函数,同时对图像进行恢复。使用混合高斯模型对核函数建模,使用自然图像的边缘大尾巴分布对图像进行约束。通过冲击滤波器预测图像的强边缘,对图像的边缘与核函数进行约束,从而更好地估计核函数。并通过迟滞阈值方法和核函数重新定位的方法,降低核函数的噪声,提高核函数估计的鲁棒性能。在求解核函数能量方程时,采用共轭梯度法,利用图像的一阶和二阶偏导数降低系统方程的条件数,加快收敛速度。最后,在一个国际公开的包含32组运动模糊图像的数据集上验证了该方法。实验结果表明,该方法所恢复的图像,其边缘和纹理清晰,能够很好地避免噪声和振铃走样问题。

Kernel estimation and reconstruction for motion blurred images

Motion blur is a common problem in hand held photography, for which this paper presented an efficient and robust method for kernel function estimation and image deblurring. It gave an image blurred by camera motion, first built an image pyramid, and then iteratively estimated the motion kernel and the latent image in a top-down manner. It modeled the motion kernel by a mixture of Gaussian, constrained and restored the latent image with a heavy-tailed distribution of natural images, and predicted the strong edges of the latent image by a shock filter, constraining the gradients and the motion kernel of the image. Moreover, it introduced a hysteresis thresholding method and a re-centering method to suppress the noise of motion kernel and improve the robustness of large motion kernel estimation. At last, it solved the kernel function using the conjugate gradient method with the first order and the second order of image derivatives to reduce the condition number, which consequently accelerated the convergence. Finally, it tested the proposed method on a publically available data set with 32 motion blurred images. As showed in the results, the proposed algorithm restores high quality latent images with clear edges and textures, free from the ringing artifacts and noises.