We present a Semi-Blind method for image deconvolution. This method uses a pre-processed image (via the shock filter) as an initial condition for total variation (TV) minimizing blind deconvolution. Using shock filter gives good information on location of the edges, and using variational functional such as Chan and Wong [T.F. Chan and C.K. Wong, Total variation blind deconvolution, IEEE Trans I...

In this paper, we present a new approach to adaptive blind image deconvolution based on computational reinforced learning in attractor-embedded solution space. A new subspace optimization technique is developed to restore the image and identify the blur. Conjugate gradient optimization is employed to provide an adaptive image restoration while a new evolutionary scheme is devised to generate th...

This paper deals with the problem of blind identification and deconvolution of FIR channels driven by a white input sequence with unknown variance, in an unbalanced noise environment. By using the structural properties of the covariance matrix of the input, an estimate of the channel coefficients is obtained. The subsequent deconvolution of the unknown input signal is then performed by means of...

The blurred image blind restoration is a difficult problem of image processing. The key is the estimation of the Point Spread Function and non-blind deconvolution algorithm. In this paper, we propose a fast robust algorithm based on radon transform-domain to determine the blur kernel function. Then the blurred images are restored by using a modified fast non-blind deconvolution method based on ...

We present an approach to determine suucient conditions for the global convergence of iterative blind deconvolution algorithms using nite impulse response (FIR) deconvolution lters. The novel technique, which incorporates Lyapunov's direct method, is general, exible and can be easily adapted to analyze the behaviour of many types of nonlinear iterative signal processing algorithms. Speciically,...

We present an approach to determine su cient conditions for the global convergence of iterative blind deconvolution algorithms using nite impulse response (FIR) deconvolution lters. The novel technique, which incorporates Lyapunov's direct method, is general, exible and can be easily adapted to analyze the behaviour of many types of nonlinear iterative signal processing algorithms. Speci cally,...

PURPOSE To evaluate maximum likelihood (ML) blind deconvolution as a technique for improving the repeatability of topographic height measurements obtained from scanning laser tomography (Heidelberg Retinal Tomograph [HRT]; Heidelberg Engineering, Heidelberg, Germany). METHODS ML blind deconvolution is an image-processing technique that estimates the original scene from a degraded image. This ...

Recently, a new blind adaptive deconvolution algorithm was proposed based on a new closed-form approximated expression for the conditional expectation (the expectation of the source input given the equalized or deconvolutional output) where the output and input probability density function (pdf) of the deconvolutional process were approximated with the maximum entropy density approximation tech...

The Internet of Things and specifically the Tactile Internet give rise to significant challenges for notions of security. In this work, we introduce a novel concept for secure massive access. The core of our approach is a fast and low-complexity blind deconvolution algorithm exploring a bi-linear and hierarchical compressed sensing framework. We show that blind deconvolution has two appealing f...

Blind deconvolution microscopy, the simultaneous estimation of the specimen function and the point spread function (PSF) of the microscope is an under-determined problem with non-unique solutions. The non-uniqueness is commonly avoided by enforcing constraints on both the specimen function and the PSF, such as non-negativity and band limitation. These constraints are some times enforced in ad h...