The goal of image restoration is to improve a given image in some predefined sense. Restoration attempts to recover an image by modelling the degradation function and applying the inverse process. Motion blur is a common type of degradation which is caused by the relative motion between an object and camera. Motion blur can be modeled by a point spread function consists of two parameters angle ...

We will now dig deeper into the details of this matrix factorization, and discuss the two specific bilateral representations we use: the simplified bilateral grid, and the permutohedral lattice [1]. Filtering with both the permutohedral lattice and the simplified bilateral grid works by “splatting” a value at each pixel onto a small number of vertices, performing a separable blur in the space o...

A new method, based on the normal shrink and modified version of Katssagelous and Lay, is proposed for multiscale blind image restoration. The method deals with the noise and blur in the images. It is shown that the normal shrink gives the highest S/N (signal to noise ratio) for image denoising process. The multiscale blind image restoration is divided in two sections. The first part of this pa...

The finite depth of field of a real camera can be used to estimate the depth structure of a scene. While the distance of an object from the plane in focus determines the defocus blur size, the shape of the aperture determines the shape of the blur. This blur shape can be manipulated by introducing masks into the main lens aperture. We propose an intuitive criterion to design aperture patterns f...

To use a projector anytime anywhere, a lot of projectorcamera based approaches have been proposed. In this paper, we focus on a focal correction technique, one of projector-camera based approaches, reduces an effect of defocus blur which occurs when a screen is located out of projector’s depth-of-field. We propose a novel method for estimating projector defocus blur on a planar screen without a...

Title of dissertation: Dimensionality Reduction for Hyperspectral Data David P. Widemann, Doctor of Philosophy, 2008 Dissertation directed by: Professor John Benedetto Department of Mathematics Professor Wojciech Czaja Department of Mathematics This thesis is about dimensionality reduction for hyperspectral data. Special emphasis is given to dimensionality reduction techniques known as kernel e...

We consider nonparametric estimation of marginal density functions of linear processes by using kernel density estimators. We assume that the innovation processes are i.i.d. and have infinite-variance. We present the asymptotic distributions of the kernel density estimators with the order of bandwidths fixed as h = cn−1/5, where n is the sample size. The asymptotic distributions depend on both ...

Blind image deblurring is a fundamental and challenging computer vision problem, which aims to recover both the blur kernel latent sharp from only blurry observation. Despite superiority of deep learning methods in have displayed, there still exists major challenge with various non-uniform motion blur. Previous simply take all features as input decoder, handles different degrees (e.g. large blu...