This project was carried out in order to improve our understanding of the mechanisms behind image deblurring. In order to gain a more thorough understanding of the basic processes that are employed in image deblurring, a number of references and textbooks were used to build up a background of understanding. Experiments were performed to explore the effectiveness of the basic methods. Through th...

This paper comprehensively reviews the recent development of image deblurring, including nonblind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the c...

The deblurring of Gaussian blur by inverting the action of the diffusion equation has long been known. This technique is interesting but without much practical application since the images have to be blurred by convolution with a Gaussian to be "de-blur-able" with this technique. In this paper we investigate the possibility to use this blurring-deblurring process as a pre-post-processing step i...

Single image blind deblurring has been intensively studied since Fergus et al.’s variational Bayes method in 2006. It is now commonly believed that the blurkernel estimation accuracy is highly dependent on the pursed salient edge information from the blurred image, which stimulates numerous l0-approximating blind deblurring methods via kinds of techniques and tricks. This paper, however, focuse...

The problem of blind image deblurring is more challenging than that of non-blind image deblurring, due to the lack of knowledge about the point spread function in the imaging process. In this paper, a learningbased method of estimating blur kernel under the ‘0 regularization sparsity constraint is proposed for blind image deblurring. Specifically, we model the patch-based matching between the b...

For a given camera setting, scene points that lie outside of depth of field (DOF) will appear defocused (or blurred). Defocus causes a loss in image details. To recover details from a defocused region, deblurring techniques must be employed. It is well known that deblurring quality is closely related to the defocus kernel or point-spread-function (PSF), whose shape is largely determined by the ...

An Adaptive method for Image Deblurring is presented here. Processing of image data collected from both surveillance camera and on road traffic control motor vehicle camera is a big issue because often the objects are in motion and sometimes both the objects and camera are not steady. This leads to Blurring of the image and further image processing is not possible due to the degradation of rece...

In this supplemental material, we analyze how the proposed blind image deblurring method performs on images with outliers and demonstrate the effectiveness of the proposed data fidelity term in Section 1. Section 2 provides more analysis of the proposed algorithm. We quantitatively evaluate the proposed algorithm and compare it against existing blind deblurring methods on the datasets with outl...

Deblurring with a spatially invariant kernel of arbitrary shape is a frequent problem in image processing. We address this task by studying nonconvex variational functionals that lead to diffusion-reaction equations of Perona–Malik type. Further we consider novel deblurring PDEs with anisotropic diffusion tensors. In order to improve deblurring quality we propose a continuation strategy in whic...