Abstract: Image restoration is used to in the Prior information of an image can often be used to restore the sharpness of edges. De-blurring is the process of removing blurring artefacts from images, such as blur caused by defocus aberration or motion blur. Motion blur is the apparent streaking of rapidly moving objects in a still image. A Gaussian blur is the result of blurring an image by a Gaussian function. The success of recent single-image methods partly stems from the use of various sparse priors, for either the latent images or motion blur kernels. In this thesis, we propose a novel scheme based on sparse representation to identify the blur kernel. By analyzing the sparse representation coefficients of the recovered image, we determine the angle of the kernel based on the observation that the recovered image has the most sparse representation when the kernel angle corresponds to the genuine motion angle. Then, we estimate the length of the motion kernel with Radon transform in Fourier domain. Our scheme can well handle large motion blur even when the license plate is unrecognizable by human.

 
Keywords: Image Restoration, Deblurring, Radon Transform

| DOI: 10.17148/IJIREEICE.2020.8631