Algorithm Research And Implementation Of Image Motion Deblurring

Image motion deblurring is a research hot spot in image restoration, and applied abroad in industry, military affairs, medical treatment and daily life.Manyreasons lead to image blurring,if it is caused by camera shake, or due to the image or the object relative motion fuzzy called motion blur. in recent years, lots of algorithms about image motion deblurring have been proposed, however most of them have limitations such as heavy computation, slow speed, small motion point spread function(PSF) assumption, multi-images, additional hardware, or severe artifacts such as ringing, and all of them make it far from practical use.In this paper,we introduce a fast, robust blind deconvolution based single image motion deblurring algorithm. We use shock filter to predict strong edges of latent image, and with this we can constrain image gradients and motion PSF with simple and easily optimized Gaussian Prior, and estimate motion PSF infrequency domain fast and robustly. After that, we use a heavy-tailed distribution of natural images based sparse non-blind deconvolution algorithm to restore the latent image, which can restore the salient edges and textureas well as suppresses ringing and noise. in this motion PSF estimation part, we can use conjugate gradient method to optimize the energy function, and use well conditioned image first and second derivatives with small condition number only and exclude pixel value with large condition number to make thealgorithm converge fast.Besides,we introduce a hysteresis thresholding to suppress the noise of motion PSF significantly,and use a re-center method to improve the robustness of large motion PSF estimation.The experimental results show that compared to traditional image motion deblurring,higher quality restoration,and shorter the processing time. Our algorithm can restore high quality latent images with clear edge and texture,little ringing or noise from a single motion blurred image fast and robustly.