We present a novel multiimage restoration method based on the underlying spatial probabilistic image model for astronomical image restoration if degradation obeys a linear degradation model with the unknown possibly nonhomogeneous point-spread function. The method assumes that for every ideal undegraded unobservable image several degraded observed images are available. Pixels in the vicinity of...

Iterative image restoration algorithms can better control the restoration process, but the choice of the relaxation parameters can be an inviable task. We present here a GPbased approach that stabilizes the iterative RAP-2D image restoration algorithm choosing the optimal or quasioptimal relaxation parameters that maximize the improvement signal to noise ratio of phantom images. CBERS-2 CCD ban...

Image restoration is an essential issue in high level image processing which deals with recovering of an original and sharp image using a degradation and restoration model. Through image acquisition process degradation occurs. Image restoration is used to estimate the unique image from the degraded data. The degradations include half toning, dithering and security watermarks. An iterative image...

Image Restoration is a field of Image Processing which deals with recovering an original and sharp image from a degraded image. Blind Image Deconvolution (BID) is a restoration technique where degraded image is restored in the absence of knowledge about the source of degradation of the image. It is very crucial part of image restoration to recover image without the knowledge of the reason of it...

Shooting a real world image with a camera through an optical device gives a 2-D image where at least some parts are affected by a blur and noise. Images can be blurred by atmospheric turbulence, relative motion between sensors and objects, longer exposures, and so on, but the exact cause of blurring may be unknown. Restoration of blurred noisy images (Spiros et al., 2009; 2010; Su et al., 2007)...

Spatially adaptive intensity bounds on the image estimate are shown to be an effective means of regularising the ill-posed image restoration problem. For blind restoration, the local intensity constraints also help to further define the solution, thereby reducing the number of multiple solutions and local minima. The bounds are defined in terms of the local statistics of the image estimate and ...

The goal of image restoration is to reconstruct the original scene from a degraded observation. This recovery process is critical to many image processing applications. Although classical linear image restoration has been thoroughly studied [1], [2], the more di cult problem of blind image restoration has numerous research possibilities. Blind image restoration is the process of estimating both...

Most of the techniques for image restoration are based on some known degradation models. But in many situations it is difficult to accurately measure the degradation factors or noise type that is the real motivation behind the use of blind deconvolution technique for image restoration. Here the observed degraded image is restored without having any prior knowledge about the noise type. Most of ...

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