Design and Analysis of Image Restoration Algorithm using Radon Transform and Bilateral Filter

The major task in photography is motion blur. Taking clear photos under dim light using a hand-held camera is quite challenging. If the camera is set to a long exposure time, the image gets blurred due to camera shake. On the other hand, the image is dark and noisy if it is taken with a short exposure time but with a high camera gain. By combining information extracted from both blurred and noisy images, however, we show in this paper how to produce a high quality image that cannot be obtained by simply denoising the noisy image, or deblurring the blurred image alone. To remove blur, we need to (i) judge how the image is blurred (ii) restore a natural looking image through deconvolution. Blur kernel estimation is challenging because the algorithm needs to distinguish the correct image pair from incorrect ones. Deconvolution is also difficult because the algorithm needs to restore high frequency image contents attenuated by blur. In this paper, we address a few aspects of these challenges. We introduce an algorithm that a blur kernel can be estimated by analyzing blurred edges. we can recover the blur kernel using the inverse Radon transform. This method is good and is well suited to images with many edges.. We introduce a method to integrate this information into a maximum-a-posteriori kernel estimation framework, and show its benefits. In this paper we compare Restored Gaussian Blurred Images by using four types of techniques of deblurring images such as Wiener filter, Inverse filter, Lucy Richardson deconvlution algorithm and our purposed algorithm on the basis of Well-known image quality accessment parameters like mean squared error (MSE) and peak signal-to-noise ratio (PSNR).