Blind Deconvolution with Canny Edge Detection: an Efficient Method for Deblurring

This paper tries to understand the study of Restored Motion Blurred Images by using four types of deblurring methods: Regularized filter, Wiener filter, Lucy Richardson and Blind Image Deconvolution. There are some indirect restoration techniques like Regularized filtering, Weiner filtering, LR Filtering in which restoration results are obtained after number of iterations. The problem of such method is that they require knowledge of blur function that is point spread function (PSF), which is unfortunately unknown when dealing with image deblurring. In this paper Blind deconvolution for image restoration is discussed which restores blurred image when the blur kernel is unknown. The fundamental task of image deblurring is to de-convolute the blurred/degraded image with the PSF that exactly describes the distortion. Firstly the original image is degraded using the Degradation Model. They can be performed by Gaussian filter which is the low-pass filter used to blur an image. There is an edge of the blurred image; the ringing effect can be detected using Canny Edge Detection method. The ringing effect is reduced by weighting function. Image restoration is concerned with the reconstruction of blur parameters of the uncorrupted image from a blurred and noisy one. Blind Deconvolution algorithm can be used effectively when no information about the blurring and noise is known. The aim of this paper to show the effective Blind Deconvolution algorithm which can effectively remove complex motion blurring from natural images without requiring any prior information of the motion-blur kernel. Keywords— Blurred image, Blind Deconvolution, Motion blur, PSF, LucyRichardson Algorithm, Regularized Filter, Weiner Filter