Biomedical images are often degraded during image acquisition. Two very common degradations that occur in many biomedical imaging modalities are blur and noise. Corrupted images are more difficult to interpret by the physician or researcher and so the goal is to restore the original image. Blurring can be mathematically modeled by convolution, and within this model the blur is characterized by ...

In blind motion deblurring, leading methods today tend towards highly non-convex approximations of the l0-norm, especially in the image regularization term. In this paper, we propose a simple, effective and fast approach for the estimation of the motion blur-kernel, through a bi-l0-l2-norm regularization imposed on both the intermediate sharp image and the blur-kernel. Compared with existing me...

This paper presents a new local feature scheme for image matching between a strongly blurred image and a non-blurred image. In recent years, a lot of local feature schemes have been proposed to improve the image matching performances. However, as far as the authors know, there are no local features which are robust to strong blur. In this paper, blur moment invariants are introduced into a loca...

Most conventional digital cameras use single capture to get an image of the scene. If long exposuretime is used, not only bright regions in the scene saturate, but also motion blur causes furtherdegradation in the image quality. If short exposure time is used, motion blur problem is not thatsevere, but the SNR of the final image is poor. Recent work has shown the availability of...

FamilySearch captures millions of digital images annually using digital cameras at sites throughout the world. The top image quality problem encountered during this image capture process is blurriness due to an out of focus camera and/or motion during capture. Several automatic measurements of digital image blur exist, but have been found to not be very accurate at correctly classifying blurry ...

In order to restore a degraded image, the first step is to identify the kind of degradation the image has suffered. Modeling the blurred image as the output of a two-dimensional (2D linear space-invariant (LSI) system, the identification problem is the estimation of the unknown point-spread function (PSF) of the LSI system. One approach to blur identification is to obtain the blur parameters (i...

A simple search method for finding a blur convolved in a given image is presented. The method can be easily extended to a large blur. The method has been experimentally tested with a model blurred image.

This paper tries to understand the study of Restored Motion Blurred Images by using four types of deblurring methods: Regularized filter, Wiener filter, Lucy Richardson and Blind Image Deconvolution. There are some indirect restoration techniques like Regularized filtering, Weiner filtering, LR Filtering in which restoration results are obtained after number of iterations. The problem of such m...

BACKGROUND The image formed by the eye's optics is inherently blurred by aberrations specific to an individual's eyes. We examined how visual coding is adapted to the optical quality of the eye. METHODS AND FINDINGS We assessed the relationship between perceived blur and the retinal image blur resulting from high order aberrations in an individual's optics. Observers judged perceptual blur in...

A space variant neural network based on an autoregressive moving average (ARMA) process is proposed for blind image deconvolution. An extended cost function motivated by human visual perception is developed to simultaneously identify the blur and restore the image degraded by space variant non-causal blur and additive white Gaussian noise (AWGN). Since the blur affects various regions of the im...