Camera shake causes images to be blurry. There are many techniques to de-blur an image. The process of removing image blur is called de-convolution. There are many different techniques for performing the de-convolution of images. In this paper, we propose a parallel de-blur algorithm inspired by Fergus et al. in [1]. The algorithm is performed by first estimating the blur kernel, then de-convol...

To improve the spatial resolution of low resolution image with Gaussian blur and Pepper & salt noise, a blind single-image super resolution reconstruction method is proposed. In the low resolution imaging model, the Gaussian blur, down-sampling, as well as Pepper & Salt noise are all considered. Firstly, the Pepper & Salt noise in the low resolution image is reduced through median filtering met...

The recent rapid popularization of digital cameras allows people to capture a large number of digital photographs easily, and this situation makes automatic avoidance and correction of “failure” photographs important. While exposure and color issues have been mostly resolved by the improvement in automatic corrective functions of cameras, defocus, motion, and camera shake blur can be handled on...

A new motion analysis method and an image restoration process for removing motion blur are proposed. Motion analysis includes the motion estimation and motion-based segmentation. Based on the analysis, we can obtain an image divided into multiple segments with different point spread functions. For removing motion blur, we propose an image degradation model for the motion with an arbitrary direc...

Object movement during exposure generates blur. Removing blur is challenging because one has to estimate the motion blur, which can spatially vary over the image. Even if the motion is successfully identified, blur removal can be unstable because the blur kernel attenuates high frequency image contents. We address the problem of removing blur from objects moving at constant velocities in arbitr...

Image processing is a process to digitize the data of an image and a variety of mathematical operations are applied to enhance the image which is more applicable or pleasing to a human observer or to accomplish some of the analysis and identification tasks by computer. Due to environmental disarray or improper camera setting blur may arise in an image. Noise can also degrade the quality of a ca...

Two frameworks for blurred image classification based on adaptive dictionary are proposed. Given a blurred image, instead of image deblurring, the semantic category of the image is determined by blur insensitive sparse coefficients calculated depending on an adaptive dictionary. The dictionary is adaptive to an assumed space invariant Point Spread Function (PSF) estimated from the input blurred...

In this paper, an image quality assessment technique based on defocus blur identification is proposed. Some representative image regions containing edge features are first extracted automatically. A histogram analysis based on the comparison of real and synthesized defocused regions is then carried out to estimate the blur extent. By iteratively changing the convolution parameters, the best blu...