A variational method for bayesian blind image deconvolution

ABSTRACT In this paper the blind image deconvolution (BID

Removal of Compression Artifacts Using Projections onto Convex Sets and Line Process Modeling

In this paper we present a new image recovery algorithm to remove, in addition to blocking, ringing artifacts from compressed images and video. This new algorithm is based on the theory of projections onto convex sets (POCS). A new family of directional smoothness constraint sets is defined based on line processes modeling of the image edge structure. The definition of these smoothness sets also takes into account the fact that the visibility of compression artifacts in an image is spatially-varying. To overcome the numerical difficulty in computing the projections onto these sets, a divide-and-conquer (DAC) strategy is introduced. According to this strategy new smoothness sets are derived such that their projections are easier to compute. The effectiveness of the proposed algorithm is demonstrated through numerical experiments using MPEG-based coders-decoders (codecs). I Introduction At the present time transform-based compression is among the most popular and it is widely used for b..

Restoration of color images by multichannel Kalman filtering

A Kalman filter for optimal restoration of multichannel images is presented. This filter is derived using a multichannel semicausal image model that includes between-channel degradation. Both stationary and nonstationary image models are developed. This filter is implemented in the Fourier domain and computation is reduced from O(Λ3N3M4) to O(Λ3N3M2) for an M × M N-channel image with degradation length Λ. Color (red, green, and blue (RGB)) images are used as examples of multichannel images, and restoration in the RGB and YIQ domains is investigated. Simulations are presented in which the effectiveness of this filter is tested for different types of degradation and different image model estimates.link_to_subscribed_fulltex

DIGITAL RESTORATION OF MULTI-CHANNEL IMAGES.

The Wiener solution of a multichannel restoration system is presented. Using block Toeplitz to block circulant approximation, the inversion of the multichannel imaging system becomes feasible by utilizing a fast iterative matrix-inversion procedure. The restoration uses both the within-channel and between-channel correlation, hence the restored result is a better estimate than the one produced by independent channel restoration. Simulations are also presented.link_to_subscribed_fulltex

Digital least squares restoration of multi-channel images

Summary form only given. A least-squares filter for the restoration of multichannel images is presented. The process involves the removal of noise and degradation from observed multichannel imagery, such as color or multispectral images. The restoration filters utilize information distributed across image channels and process all channels as a single entity. They use a priori information and constraints, thus avoiding some of the drawbacks of the minimum-mean-squared-error filter.link_to_subscribed_fulltex

Digital restoration of multichannel images.

The Wiener solution of a multichannel restoration scheme is presented. Using matrix diagonalization and block-Toeplitz to block-circulant approximation, the inversion of the multichannel, linear space-invariant imaging system becomes feasible by utilizing a fast iterative matrix inversion procedure. The restoration uses both the within-channel (spatial) and between-channel (spectral) correlation; hence, the restored result is a better estimate than that produced by independent channel restoration. Simulations are also presented.link_to_subscribed_fulltex

An Unsupervised Artifact Correction Approach for the Analysis

Image processing for analysis of microarray images is an important and challenging problem because imperfections and fabrication artifacts often impair our ability to measure accurately the quantities of interest in these images. In this paper we propose a microarray image analysis framework that provides a new method that automatically addresses each spot area in the image. Then, a new unsupervised clustering method is used which is based on a Gaussian mixture model (GMM) and the minimum description length (MDL) criterion, that allows the automatic spot area segmentation and the image artifacts isolation and correction to obtain more accurate spot quantitative values. Experimental results demonstrates the advantages of the proposed scheme in efficiently analysing microarrays. 1

Regularized Constrained Total Least-Squares Image Restoration

In this paper the problem of restoring an image distorted by a linear space-invariant (LSI) point-spread function (psf) which is not exactly known is formulated as the solution of a perturbed set of linear equations. The regularized constrained total least-squares (RCTLS) method is used to solve this set of equations. Using the diagonalization properties of the discrete Fourier transform (DFT) for circulant matrices, the RCTLS estimate is computed in the DFT domain. This significantly reduces the computational cost of this approach and makes its implementation possible even for large images. An error analysis of the RCTLS estimate, based on the mean-squared-error (MSE) criterion is performed to verify its superiority over the constrained total least-squares (CTLS) estimate. Numerical experiments for different psf errors are performed to test the RCTLS estimator for this problem. Objective and visual comparisons are presented with the linear minimum mean-squared-error (LMMSE) and the re..

Deformation Analysis of the Vocal Folds From Videostroboscopic Image Sequences of the Larynx

Videostroboscopy is an examination which yields a permanent record of the moving vocal folds. Thus, it allows the diagnosis of abnormalities which contribute to voice disorders. In this paper, in order to find and quantify the deformation of the vocal folds in videostroboscopic recordings, an active contours (snakes) based approach is used to delineate the vocal folds in each frame of the videostroboscopic image sequence. After this delineation, a new elastic registration algorithm is used to register the vocal fold contours between adjacent frames of the video sequence. This algorithm is based on the regularization principle and is very effective when large deformations are present. A least-squares approach is used to fit an affine model to the displacement vectors found by elastic registration. The parameters of this model, rotation, translation, and deformation along two principle axes, quantify the deformation and allow the succinct characterization of the videostroboscopic recordi..