Image Deblurring with Sparse Representation Essay Example

received a lot of attention in computer vision community. Deblurring is the combination of two sub-problems: Point spread function (PSF) estimation and non-blind image deconvolution. These problems are both independently in computer graphics, computer vision, and image processing.

Finding a sparse representation of input data in the form of a linear combination of basic elements. It is called sparse dictionary learning and this is learning method. These elements are compose a dictionary. Atoms in the dictionary are not required to be orthogonal . One of the key principles of dictionary learning is that the dictionary has to be inferred from the input data. The sparse dictionary learning method has been stimulated by the signal processing to represent the input data using as few possible components.

To unblurred an image the non-blind deconvolution blur Point Spread Function (PSF) has been used. The previous works to restore an image based on Richardson-Lucy (RL) or Weiner tering have more noise sensitivity. Total Variation regularizer heavy-tailed normal image priors and Hyper-Laplacian priors were also widely studied. Blind deconvolution can be performing iteratively, whereby each iteration improves the estimation of the PSF .

In found that a new iterative optimization to solve the kernel estimation of images. To deblur images with very large blur kernels is very difficult. to reduce this difficulty using the iterative methods to deblur the image. Fromfound that to solve the kernel estimation and large scale optimization is used unnatural l0 sparse representation. The properties for latent text image and the difficulty of applying the properties to text image de-blurring is discussed in. Two motion blurred images with different blur directions and its restoration quality is superior than when

using only a single image. A deblurring methods can be modelled as the observed blurry image as the convolution of a latent image with a blur kernel.

The camera moves primarily forward or backward caused by a special type of motion blur it is very difficult to handle. To solve this type of blur is distinctive practical importance. A solution to solve using depth variation. The feature-sign search for solving the l1-least squares problem to learn coefficients of problem optimization and a Lagrange dual method for the l2-constrained least squares problem to learn the bases for any sparsity penalty function.