This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and feature-based) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas. q 2003 Elsevier B.V. All rights reserved.

ABSTRACT Since their introduction in a classic paper by Rudin, Osher and Fatemi [26], total variation minimizing models have become one of the most popular and successful methodology for image restoration. More recently, there has been a resurgence of interest and exciting new developments, some extending the applicabilities to inpainting, blind deconvolution and vector-valued images, while others offer improvements in better preservation of contrast, geometry and textures, in ameliorating the staircasing effect, and in exploiting the multiscale nature of the models. In addition, new computational methods have been proposed with improved computational speed and robustness. We shall review some of these recent developments. 1

In this paper, the blind image deconvolution (BID) problem is addressed using the Bayesian framework. In order to solve for the proposed Bayesian model, we present a new methodology based on a variational approximation, which has been recently introduced for several machine learning problems, and can be viewed as a generalization of the expectation maximization (EM) algorithm. This methodology reaps all the benefits of a "full Bayesian model" while bypassing some of its difficulties. We present three algorithms that solve the proposed Bayesian problem in closed form and can be implemented in the discrete Fourier domain. This makes them very cost effective even for very large images. We demonstrate with numerical experiments that these algorithms yield promising improvements as compared to previous BID algorithms. Furthermore, the proposed methodology is quite general with potential application to other Bayesian models for this and other imaging problems.

Abstract—Despite its practical importance in image processing and computer vision, blind blur identification and blind image restoration have so far been addressed under restrictive assumptions such as all-pole stationary image models blurred by zero- or minimum-phase point-spread functions. Relying upon diversity (availability of a sufficient number of multiple blurred images), we develop blind FIR blur identification and order determination schemes. Apart from a minimal persistence of excitation condition (also present with nonblind setups), the inaccessible input image is allowed to be deterministic or random and of unknown color or distribution. With the blurs satisfying a certain co-primeness condition in addition, we establish existence and uniqueness results which guarantee that single-input/multiple-output FIR blurred images can be restored blindly, though perfectly in the absence of noise, using linear FIR filters. Results of simulations employing the blind order determination, blind blur identification, and blind image restoration algorithms are presented. When the SNR is high, direct image restoration is found to yield better results than indirect image restoration which employs the estimated blurs. In low SNR, indirect image restoration performs well while the direct restoration results vary with the delay but improve with larger equalizer orders. Index Terms—Blind blur estimation, blind image restoration, multichannel image restoration. I.

Blind system identification is a fundamental signal processing technology aimed to retrieve unknown information of a system from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation and blind image restoration. This paper reviews a number of recently developed concepts and techniques for blind system identification which include the concept of blind system identifiability in a deterministic framework, the blind techniques of maximum likelihood and subspace for estimating the system's impulse response, and other techniques for direct estimation of the system input. Keywords: System identification, Blind techniques, Multichannels, Equalization, Source separation. This work has been supported by the Australian Research Council and the Australian Cooperative Research Center for Sensor Signal and Information Processing. y Currently with Motorola Australian Research Centre, 12 Lord Street, Botany 2019, ...

Segmentation and blind restoration are both classical problems, that are known to be di#cult and have attracted major research e#orts. This paper shows that the two problems are tightly coupled and can be successfully solved together. Mutual support of the segmentation and blind restoration processes within a joint variational framework is theoretically motivated, and validated by successful experimental results.

Thanks to its ability to yield functionally rather than anatomically-based information, the threedimensional (3D) SPECT imagery technique has become a great help in the diagnostic of cerebrovascular diseases. Nevertheless, due to the imaging process, the 3D SPECT images are very blurred and consequently their interpretation by the clinician is often difficult and subjective. In order to improve the resolution of these 3D images and then to facilitate their interpretation, we propose herein, to extend a recent image blind deconvolution technique (called the NAS-RIF deconvolution method) in order to improve both the spatial and the inter-slice resolution of SPECT volumes. This technique requires a preliminary step in order to find the support of the object to be restored. In this paper, we propose to solve this problem with an unsupervised 3D Markovian segmentation technique. This method has been successfully tested on numerous real and simulated brain SPECT volumes, yielding very promis...

INTRODUCTION Images are produced to record or display useful information. Due to imperfections in the imaging and capturing process, however, the recorded image invariably represents a degraded version of the original scene. The undoing of these imperfections is crucial to many of the subsequent image processing tasks. There exists a wide range of different degradations that need to be taken into account, covering for instance noise, geometrical degradations (pin cushion distortion), illumination and color imperfections (under/over-exposure, saturation), and blur. This chapter concentrates on basic methods for removing blur from recorded sampled (spatially discrete) images. There are many excellent overview articles, journal papers, and textbooks on the subject of image restoration and identification. Readers interested in more details than given in this chapter are referred to [2, 3, 9, 11, 14]. Blurring is a form of bandwidth reduction of an ideal image owing to the imperfe

To recover a sharp image from its blurry observation is the problem known as image deblurring. It frequently arises in imaging sciences and technologies, including optical, medical, and astronomical applications, and is crucial for allowing to detect important features and patterns such as those of a distant planet or some microscopic tissue. Mathematically, image deblurring is intimately connected to backward diffusion processes (e.g., inverting the heat equation), which are notoriously unstable. As inverse problem solvers, deblurring models therefore crucially depend upon proper regularizers or conditioners that help secure stability, often at the necessary cost of losing certain highfrequency details in the original images. Such regularization techniques can ensure the existence, uniqueness, or stability of deblurred images. The present work follows closely the general framework described in our recent monograph [18], but also contains more updated views and approaches to image deblurring, including, e.g., more discussion on stochastic signals, the Bayesian/Tikhonov approach to Wiener filtering, and the iterated-shrinkage algorithm of Daubechies et al. [30,31] for wavelet-based deblurring. The work thus contributes to the development of generic, systematic, and unified frameworks in contemporary image processing.

We propose a total variation based model for simultaneous image inpainting and blind deconvolution.