A pair of noisy/blurry patches-based PSF estimation and channel-dependent deblurring

A camera shake of the relative motion between cameras and original scenes often occurs during a shoot, resulting in blurred images. This paper presents a method of restoring blurred images based on patch-based PSF (Point Spread Function) estimation and channel-dependent image deblurring. Initially, a pair of noisy and blurred images is acquired by consecutive shooting under short and long exposures to more accurately estimate the 2D trajectory formed by the camera shake, which is also known as PSF. From the noisy image, one patch with strong edges is extracted using gradient operators and the corresponding blurry patch is found within the blurry image. The boundaries of both patches are padded in a specified tile pattern to prevent the appearance of vertical or horizontal lines due to boundary discontinuity. Tikhonov regularization is then iteratively conducted on Gaussian pyramid images with adaptive noise thresholding to get the optimized PSF. Finally, the color conversion of the blurred image into a luminance and two chrominance images is done to apply the channel dependent image deblurring where the spatial resolution of the chrominance images is reduced based on the natural gradient distribution to abandon the valueless data regardless of the restoration quality. Moreover, another artificially blurred image made by the convolution of the known Gaussian PSF and the noisy image is used to reduce undesirable ringing-artifacts and increase the sharpness. The experimental results show that the proposed method can provide more improved PSFs and restored images accompanied by the reduction of computing time and ring artifacts.