Multi-resolution deblurring

As technology advances; blur in an image remains as an ever-present issue in the image processing field. A blurred image is mathematically expressed as a convolution of a blur function with a sharp image, plus noise. Removing blur from an image has been widely researched and is still important as new images are collected. Without a reference image, identifying, measuring, and removing blur from a given image is very challenging. Deblurring involves estimating the blur kernel to match with various types of blur including camera motion/de focus or object motion. Various blur kernels have been studied over many years, but the most common function is the Gaussian. Once the blur kernel (function) is estimated, a deconvolution is performed with the kernel and the blurred image. Many existing methods operate in this manner, however, these methods remove blur from the blurred region, but alter the un-blurred regions of the image. Pixel alteration is due to the actual intensity values of the pixels in the image becoming easily distorted while being used in the deblurring process. The method proposed in this paper uses multi-resolution analysis (MRA) techniques to separate blur, edge, and noise coefficients. Deconvolution with the estimated blur kernel is then performed on these coefficients instead of the actual pixel intensity values before reconstructing the image. Additional steps will be taken to retain the quality of un-blurred regions of the blurred image. Experimental results on simulated and real data show that our approach achieves higher quality results than previous approaches on various blurry and noise images using several metrics including mutual information and structural similarity based metrics.