Free viewpoint television (FTV) is a new concept that aims at giving viewers the flexibility to select a novel viewpoint by employing multiple video streams as the input. Current proposed solutions for FTV include those based on ray-space resampling which demand at least dozens of cameras and large storage and transmission resources for those video streams. Image-based rendering (IBR) methods that rely on dense depth map estimation also face practical difficulties since accurate depth map estimation remains a challenging problem. This paper proposes a framework for FTV based on IBR that relieves the need for an accurate depth map by introducing a hybrid virtual view synthesis method. The framework also includes an intuitive method for virtual view specification in uncalibrated views. We present both simulation and real data experiments to validate the proposed framework and the component algorithms.

In this paper, we describe a presence based collaborative recommender (PBCR) system for networked audiovisual (AV) displays such as Internet connected TV sets with access to broadcast TV programs over traditional channels, video on demand, and Internet Protocol (IP) AV programs. The proposed PBCR system is based on presence technology and provides viewers with collaborative recommendations on AV programs based on presence or ratings of users within viewer's community.

We address the problem of robust streaming of high-quality video over wireless local area networks in a home environment. By robust streaming, we mean maintaining the highest possible video quality and preventing interruptions to the video under varying bandwidth conditions, which may be due to distance, interference, obstructions, and existence of multiple streams. We propose an application-layer approach where we provide algorithms for dynamic on-line network bandwidth estimation and dynamic on-line adaptation of video rate according to the available network bandwidth. The proposed system employs a packet scheduler, and a video rate control and adaptation mechanism at the sender, and bandwidth measurement and feedback mechanisms at the receiver. Our bandwidth estimation approach uses the actual video data in real time by transmitting it in packet bursts; hence, separate test traffic is not required. Since the proposed method operates at the application layer, it is flexible and applicable to different local area network types and implementations. We propose an extension to multiple streams by providing an algorithm for joint rate allocation to multiple video streams over a network enabling network-adaptive simultaneous streaming of high-quality video.

We propose a general framework for indexing and summarizing sports broadcast programs and its specific application to soccer. The framework is based on a high-level model of sports broadcast video using the concept of an event, defined according to domain-specific knowledge for different types of sports. Thus it covers most sports including those that have the action-and-stop pattern (e.g., baseball) and those containing continuous actions (e.g., soccer). In particular, within this general framework, using soccer as an example, we propose a novel approach to automatic event detection, which is based on automatic analysis of the visual and aural signals in the media. An MPEG-7 compliant prototype browsing system has been implemented to demonstrate the results.

We propose novel algorithms for automatically determining a user's profile from his/her content usage history (profiling agent), and automatically filtering content according to the user's profile (filtering agent). A fuzzy inference system is used to construct and periodically update the preferences of a user based on the user's interactions with various types of content over an observation period. The proposed algorithms are designed to support an MPEG-7 or TV-anytime-compliant description framework, although they can also be utilized in any non-standard environment that provides structured descriptions of multimedia content.

In broadcast sports, replays provide viewers another look at interesting events. We propose an automatic algorithm for replay segment detection by detecting frames containing logos in the special scene transitions that sandwich replays. Detected replays are utilized in efficient navigation, indexing, and summarization of sports programs. The proposed algorithm first automatically determines the logo template from frames surrounding slow motion segments, where slow motion segments are automatically detected using [4]. Then, it locates all the similar frames in the video using the logo template. Finally the algorithm identifies the replay segments by grouping the detected logo frames and slow-motion segments. Our algorithm accurately detects replays, with or without slow motion.

We present a novel method for generating sports video summary highlights. Specifically, our method localizes semantically important events in sport programs by detecting slow motion replays of these events, and then generates highlights of these events at multiple levels. In our method, a hidden Markov model (HMM) is used to model slow motion replays, and an inference algorithm is introduced which computes the probability of a slow motion replay segment, and localizes the boundaries of the segment as well. An effective new feature is used in our HMM, based on a moving measure of the number of zero-crossings and the amplitudes of variations over time of video field differences. Furthermore, the method is capable of filtering out slow motion play segments in commercials. As compared with existing methods for video event detection, our method is more generic (ie, domain independent), and has the ability to capture inherently important events.

A video background replacement algorithm is proposed, which is based on background subtraction with adaptive background modeling. Like one early work ([1]) in this direction, it does not require a blue screen but needs only a pre-recorded background scene image. The problem is formulated as one that detects statistical outliers with respect to the given background. A two-pass process, which refines initial segmentation based on the statistics on a pixel’s neighborhood, is adopted in order to suppress false positives in the background region while increasing detection rate for the foreground object. Experiments with real image sequences are presented, along with comparisons with some other existing methods, illustrating the advantages of the proposed algorithm. 1.

Most consumer cameras use a single CCD sensor and a colour filter array (CFA) to take colour pictures. The array samples the red, green, and blue colour planes on sparse sampling lattices that are not coincident and not necessarily orthogonal. The "raw" images are then interpolated and post-processed to prepare them for display. Existing multiframe enhancement methods use the post-processed colour frames. We present a new multi-image enhancement algorithm which uses the raw image sequence directly to produce improved enhanced-resolution enlargements. We also demonstrate the limitations to resolution enhancement that occur when performing enhanced enlargement on low-resolution CFA-interpolated sequences.

We propose a three-level video-event detection methodology and apply it to animal-hunt detection in wildlife documentaries. The first level extracts color, texture, and motion features, and detects shot boundaries and moving object blobs. The mid-level employs a neural network to determine the object class of the moving object blobs. This level also generates shot descriptors that combine features from the first level and inferences from the mid-level. The shot descriptors are then used by the domain-specific inference process at the third level to detect video segments that match the user defined event model. The proposed approach has been applied to the detection of hunts in wildlife documentaries. Our method can be applied to different events by adapting the classifier at the intermediate level and by specifying a new event model at the highest level. Event-based video indexing, summarization, and browsing are among the applications of the proposed approach.

We propose a three-level video event detection algorithm and apply it to animal hunt detection in wildlife documentaries. The first level extracts texture, color and motion features, and detects motion blobs. The mid-level employs a neural network to verify whether the motion blobs belong to objects of interest. This level also generates shot summaries in terms of intermediate-level descriptors which combine low-level features from the first level and contain results of mid-level, domain specific inferences made on the basis of shot features. The shot summaries are then used by a domain-specific inference process at the third level to detect the video segments that contain events of interest, e.g., hunts. Event based video indexing, summarization and browsing are among the applications of the proposed approach.

An algorithm is presented for replacing video background without a physical blue screen. The method requires pre-recording a background image of the scene without the presence of any foreground objects. Based on the color difference between the pixels in an input frame and their corresponding pixels in the background image, the algorithm computes a probability map which contains the likelihood for each pixel to be classified into the foreground or background. The probability map is further refined using anisotropic diffusion which reduces classification errors without introducing significant artifacts. Finally, based on the refined probability map, a new video is synthesized by feathering the foreground pixels of the input frames onto a new background video or image. The algorithm requires moderate computation resources and has been implemented to run in real time. Experimental results are presented.

We address the problem of on-line sprite-based video coding in cases where scene segmentation is not available a priori or transmission of such segmentation information cannot be afforded due to low bit rate requirements. We propose an on-line segmentation method that can be integrated into an MPEG4 on-line sprite based video codec. The proposed method uses macroblock types as well as motion compensated residuals to perform the on-line segmentation. It produces a background mosaic without requiring a priori foreground-background segmentation information. Our results demonstrate the coding efficiency and functionality benefits of the proposed approach.

This paper presents a statistics-based method for estimating the position and size of a face in a complex background. Face position and size are estimated based on robust statistical measurements which are derived from two one-dimensional histograms obtained by projecting the result of skin color filtering. The proposed algorithm also utilizes a linear Kalman filter and a simple nonlinear filter to perform smooth tracking and remove jitter. The algorithm has been implemented and tested under a wide range of real-world conditions. It has consistently provided performance which satisfies the following requirements: (1) the ability to automatically determine the initial position and size of a face and track it in a complex background; (2) is insensitive to partial occlusions and shadows; (3) is insensitive to face orientation and scale changes; and (4) is also insensitive to lighting condition changes. In addition, the algorithm is computationally simple so that it can be executed in real-time.

We propose a new approach for motion-compensated, reduced order model Kalman filtering for restoration of progressive and interlaced video. In the case of interlaced inputs, the proposed filter also performs deinterlacing. In contrast to the literature, both motion-compensation and reduced-order state modeling are achieved by augmenting the observation equation, as opposed to modifying the state-transition equation. The proposed modeling, which includes the two-dimensional (2-D) reduced order model Kalman filtering (ROMKF) of Angwin and Kaufman as a special case, results in significant performance improvement in fixed-lag Kalman filtering of space-varying blurred images. This is demonstrated by experimental results.

In this paper, we present new methods for content-scalable shape representation and coding where different parts of a certain shape can be represented and coded at varying quality levels. We present a hierarchical vertex selection algorithm and develop a vertex-based content-scalable contour representation and coding method. In addition, we show how content-scalable shape coding can be achieved by modifying the leading bitmap-based shape coding methods. Experimental results are given in the paper.

We present robust algorithms which combine global motion compensation and motion adaption for deinterlacing in the presence of both dominant motion, such as camera zoom, pan, or jitter, and local motion, such as object motion. The dominant motion is modeled by a global affine warping and estimated by a gradient-based estimation method. Two alternative algorithms are proposed for compensation of the dominant motion: a bilinear interpolation based on the affine model, and a projections onto convex sets (POCS) based method that takes into account blurring in the image formation. It is important to note that the latter must be used if the blurring is severe enough to act as an anti-alias filter, which imposes an irreversible limit on the resolution improvement ability of any motion-compensated filter. Global motion-compensated images are then input to a motion-adaptive filter to detect and correct for those pixels where there exists local motion. A dynamic thresholding for motion detection is presented, with weighted directional-filtering for regions where motion is detected, to obtain the best results. Experimental results with application to obtaining high quality stills from video camcorders demonstrate the effectiveness of the proposed methods.

We present a Bayesian framework that combines motion (optical flow) estimation and segmentation based on a representation of the motion field as the sum of a parametric field and a residual field. The parameters describing the parametric component are found by a least squares procedure given the best estimates of the motion and segmentation fields. The motion field is updated by estimating the minimum-norm residual field given the best estimate of the parametric field, under the constraint that motion field be smooth within each segment. The segmentation field is updated to yield the minimum-norm residual field given the best estimate of the motion field, using Gibbsian priors. The solution to successive optimization problems are obtained using the highest confidence first (HCF) or iterated conditional mode, (ICM) optimization methods. Experimental results on real video are shown.

We propose a robust, object-based approach to high-resolution image reconstruction from video using the projections onto convex sets (POCS) framework. The proposed method employs a validity map and/or a segmentation map. The validity map disables projections based on observations with inaccurate motion information for robust reconstruction in the presence of motion estimation errors; while the segmentation map enables object-based processing where more accurate motion models can be utilized to improve the quality of the reconstructed image. Procedures for the computation of the validity map and segmentation map are presented. Experimental results demonstrate the improvement in image quality that can be achieved by the proposed methods.

Printing from an NTSC source and conversion of NTSC source material to high-definition television (HDTV) format are some of the applications that motivate superresolution (SR) image and video reconstruction from low-resolution (LR) and possibly blurred sources. Existing methods for SR image reconstruction are limited by the assumptions that the input LR images are sampled progressively, and that the aperture time of the camera is zero, thus ignoring the motion blur occurring during the aperture time. Because of the observed adverse effects of these assumptions for many common video sources, this paper proposes (i) a complete model of video acquisition with an arbitrary input sampling lattice and a nonzero aperture time, and (ii) an algorithm based on this model using the theory of projections onto convex sets to reconstruct SR still images or video from an LR time sequence of images. Experimental results with real video are provided, which clearly demonstrate that a significant increase in the image resolution can be achieved by taking the motion blurring into account especially when there exists large interframe motion.

We propose a region-based approach to high-resolution image reconstruction from video using the projections onto convex sets framework. The region-based formalism allows the use of a validity map and/or a segmentation map. The validity map disables projections based on observations with inaccurate motion information, for robust reconstruction in the presence of motion estimation errors; while the segmentation map enables object-based processing where more accurate motion models can be utilized to improve the reconstruction quality of image object. A procedure for the computation of the validity map is presented. Experimental results demonstrate the quality improvement that can be achieved by the proposed region-based approach.

Many interactive video and multimedia applications, such as interactive TV, augmented reality and bit stream editing, demand object-based video modeling. These applications require accurate tracking of the boundary, local motion and intensity variations of each object. This paper proposes a new approach to track deformable objects in video sequences, which combines deformable contours and 2-D mesh models while addressing partial occlusion, including self-occlusion. Experimental results demonstrate successful tracking of objects with deformable boundaries in the presence of self-occlusion.

We propose a method for tracking the motion and intensity variations of a 2-D mildly deformable image object using a hierarchical 2-D mesh model. The proposed method is applied to synthetic object transfiguration, namely, replacing an object in a real video clip with another synthetic or natural object via digital postprocessing. Successful transfiguration requires accurate tracking of both motion and intensity (contrast and brightness) variations of the object-to-be-replaced so that the replacement object can be rendered in exactly the same way from a single still picture. The proposed method is capable of tracking image regions corresponding to scene objects with nonplanar and/or mildly deforming surfaces, accounting for intensity variations, and is shown to be effective with real image sequences.

With the advent of frame grabbers capable of acquiring multiple video frames, a great deal of attention is being directed at creating high-resolution (hi-res) imagery from interlaced or low-resolution (low-res) video. This is a multi-faceted problem, which generally necessitates standards conversion and hi-res reconstruction. Standards conversion is the problem of converting from one spatio-temporal sampling lattice to another, while hi-res image reconstruction involves increasing the spatial sampling density. Also of interest is removing degradations that occur during the image acquisition process. These tasks have all received considerable, yet separate, treatment in the literature. A unifying video formation model is presented which addresses these problems simultaneously. Then, a POCS-based algorithm for generating high-resolution imagery from video is delineated. Results with real imagery are included.

Synthetic object transfiguration is the problem of replacing an existing object in an image sequence with a new one via digital post-processing. Successful transfiguration requires accurate tracking of both motion and intensity variations of the existing object so that the new object can be rendered in exactly the same way. We propose a 2-D mesh tracking method that estimates, on a local basis, the motion and intensity variations of a region of interest. The proposed method is capable of accounting for illumination changes, tracking image regions corresponding to scene objects with non-planar and/or mildly deforming surfaces, and is shown to be effective in real-life situations.

We present an algorithm that simultaneously performs motion estimation and scene segmentation in image sequences. This algorithm improves upon the traditional approach of using the motion estimates that are obtained separately as input for scene segmentation. Based on the maximum a posteriori probability (MAP) criterion, the interdependence of constraints for motion estimation and scene segmentation are expressed in a Gibbs distribution. The solution is obtained based on the highest confidence first (HCF) and iterated conditional mode (ICM) optimization methods. Experimental results suggest that this algorithm is of potential use for low-bit rate video coding.>

We address the standards conversion of digital video containing accelerated motion. The spectral characterization of such video is performed by means of short time spectral analysis. The support of the Short Time Spectrum (STS) of video with accelerated motion is derived, and is compared to the STS of linear time-varying (LTV) filters that operate along an accelerated motion trajectory. By utilizing the multiplication property of the STS, it is shown that filtering along the accelerated motion trajectory potentially yields the smallest amount of aliasing in standards conversion applications.>

Provides a theoretical analysis of the multigeneration properties of the MPEG video compression algorithms using the theory of generalized projections (GP). Multi-generations of an input video are obtained by successively compressing and decompressing it. The authors show that, under certain assumptions, successive generations can be interpreted as successive projections of the input video onto well-defined sets in a Hilbert space. Then, using the theory of GP, they establish that the multi-generations of I, P, and B pictures in the MPEG algorithms saturate.>

We address the problem of reconstruction of a high-resolution image from a sequence of low-resolution images containing arbitrary relative motion, excluding occlusion effects. We develop a formulation that simultaneously takes into account blurring due to relative sensor-object motion, sensor integration, and additive noise. We propose a POCS-based algorithm for performing the high-resolution reconstruction, and provide experimental results.>

We propose a new method for space-varying image restoration using the method of projection onto convex sets (POCS). The formulation allows the use of a different blurring function at each pixel of the image in a computationally efficient manner. We illustrate the performance of the proposed approach by comparing the new results with those of the ROMKF method on simulated images. We also present results on a real-life image with unknown space-varying out-of-focus blur.

A Bayesian segmentation algorithm to separate color images into regions of distinct colors is presented. The algorithm takes into account the local color variations in the image in an adaptive manner. A Gibbs random field (GRF) is used as the a priori probability model for the segmentation process to impose a spatial connectivity constraint. We study the performance of the proposed algorithm in different color spaces and its application in reduced data rendering of color images. Experimental results and discussion are included.

The authors propose a novel adaptive spatiotemporal filter, called the adaptive weighted averaging (AWA) filter, for effective noise suppression in image sequences without introducing visually disturbing blurring artifacts. Filtering is performed by computing the weighted average of image values within a spatiotemporal support along the estimated motion trajectory at each pixel. The weights are determined by optimizing a well defined mathematical criterion, which provides an implicit mechanism for deemphasizing the contribution of the outlier pixels within the spatiotemporal filter support to avoid blurring. The AWA filter is therefore particularly well suited for filtering sequences that contain segments with abruptly changing scene content due to, for example, rapid zooming and changes in the view of the camera. The performance of the proposed AWA filter is compared with that of the spatiotemporal, local linear minimum mean square error (LMMSE) filtering. The results demonstrate that the proposed AWA filter-outperforms the LMMSE filter, especially in the cases of low signal-to-noise ratios and abruptly varying scene content.>

A computationally efficient multiframe LMMSE filtering algorithm, the motion-compensated multiframe (MCMF) Wiener filter, for restoring image sequences that are degraded by both blur and noise is proposed. MCMF Wiener filter applies to the cases where each frame of the ideal image sequence can be expressed as a globally shifted version of its previous frame. As opposed to single-frame filtering, the MCMF Wiener filter accounts for interframe (temporal) correlations as well as intraframe (spatial) correlations in restoring a given image sequence. The MCMF filter requires neither the explicit estimation of cross correlations among the frames, nor any matrix inversion. It accounts for the interframe correlations implicitly by using the estimated interframe motion information. The results of an extensive study on the performance and robustness of the proposed algorithm are presented.>

Motion pictures have a temporal rate of 24 frames per second, while most of the conventional video displays and recording devices utilize a rate of 60 fields per second. In the case of high-definition television (HDTV) systems, some proposals in the US require a video rate of 60 frames per second. Upconversion of motion picture film to 60 frames per second is important since motion picture constitutes a high-quality source material for HDTV. The authors address the problem of motion-compensated upconversion of 24 frames/s digitized motion pictures to 60 frames/s digital video signals.>

The authors address the problem of reconstruction of a high-resolution image from a number of lower-resolution (possibly noisy) frames of the same scene where the successive frames are uniformly based versions of each other at subpixel displacements. In particular, two previously proposed methods, a frequency-domain method and a method based on projections onto convex sets (POCSs), are extended to take into account the presence of both sensor blurring and observation noise. A new two-step procedure is proposed, and it is shown that the POCS formulation presented for the high-resolution image reconstruction problem can also be used as a new method for the restoration of spatially invariant blurred images. Some simulation results are provided.>

A method for the estimation of the parameters of multipath communication channels is developed. The method makes use of matched filtering, set-theoretic deconvolution and autoregressive modeling. Pulse compression waveforms are used as channel probing signals. The results of a feasibility study in which the proposed method is applied to specular multipath channels to estimate the multipath parameters at a number of different signal-to-noise ratios (SNR) are presented.>

Computationally efficient multiframe Wiener filtering algorithms that account for both intraframe (spatial) and interframe (temporal) correlations are proposed for restoring image sequences that are degraded by both blur and noise. One is a general computationally efficient multiframe filter, the cross-correlated multiframe (CCMF) Wiener filter, which directly utilizes the power and cross power spectra of only NxN matrices, where N is the number of frames used in the restoration. In certain special cases the CCMF lends itself to a closed-form solution that does not involve any matrix inversion. A special case is the motion-compensated multiframe (MCMF) filter, where each frame is assumed to be a globally shifted version of the previous frame. In this case, the interframe correlations can be implicitly accounted for using the estimated motion information. Thus the MCMF filter requires neither explicit estimation of cross correlations among the frames nor matrix inversion. Performance and robustness results are given.

A method for the estimation of the parameters of specular multipath communication channels is presented. The method takes advantage of the matched filtering, set-theoretic deconvolution and autoregressive modeling. Pulse compression waveforms such as chip and frequency-hop coding waveforms are used as channel problem signals. The proposed method, applied to a number of multipath channel models with closely spaced path components, produces high-resolution estimates of multipath parameters at signal-to-noise ratios higher than 5 dB.>

A motion-compensated noise suppression algorithm that employs temporally adaptive filtering along motion trajectories is proposed for image sequences. Filtering is performed via linear minimum mean square error (LMMSE) point estimation. Motion trajectories are determined using a recent motion estimation algorithm, which is capable of performing very well at low signal-to-noise ratios (SNRs). The results suggest that the proposed method is far superior to methods that incorporate implicit or explicit motion compensation, especially in cases of low SNR and/or significant interframe motion.>

The point spread function (PSF) of a defocused lens, in the continuous spatial coordinates, can be modeled using the principles of either geometrical optics or physical optics. The authors compare the PSFs and the corresponding optical transfer functions obtained on the basis of these two models under different amounts of blurs and different imaging sensor resolutions, in the discrete spatial domain. Different approximations for the discretization of the PSF are considered. The effect of using geometrical versus physical optics-based models on the quality of restored images is investigated experimentally in the case of images recorded by a charge-coupled device (CCD) camera.>

Constraints are developed based on prototype images and used in set-theoretic image restoration. A prototype can be obtained as a result of applying a predetermined operator to the observed image. In this case, the operator and the bound, which limits the variation of the restored image from the prototype, are the two defining quantities of a prototype constraint. General guidelines for rigorously estimating the defining bound of a prototype constraint under certain simplifying conditions are discussed. Two examples of prototype constraints are provided and used in the context of projections onto convex sets. The resulting restorations show significant improvement over the Wiener restorations in reducing the restoration artifacts.>

In this review, we consider the three fundamental aspects of image restoration: (i) modeling, (ii) model identification methods, and (iii) restoration methods. Modeling refers to determining a model of the relationship between the ideal image and the observed degraded image, as well as modeling the ideal image itself, on the basis of a priori information. Model parameters are determined by various identification methods. Restoration algorithms are discussed in two categories: general algorithms and specialized algorithms. We also furnish a brief discussion of present and future research directions.

Projection method formulations of the perceptron and Hopfield associative content-addressable memory (ACAM) neural nets are presented. It is shown that the well-known single-layer-perceptron learning algorithm can be formulated using the method of projections onto convex sets (POCS) and that its performance can be improved using this method. The operation of a modified, binary-valued Hopfield ACAM is shown to be equivalent to the method of generalized projections. A direct extension of the binary-valued ACAM to the continuous-valued case lends itself to a POCS formulation.>

The use of a priori information and its effect on multiband image restoration are considered. The Wiener solution that takes into account the statistical correlations between image bands is shown to be extremely sensitive to between-channel correlation estimates. In fact, the Wiener restoration using the cross correlations, estimated from a prototype image similar to the original, may give worse results than the Wiener restoration obtained by simply ignoring the cross correlations and independently restoring the channels. With the aim of obtaining a more robust algorithm, a unified set-theoretic framework that allows the simultaneous use of single and multiband a priori information is developed. A number of convex constraint sets are provided for use in a convex projections restoration algorithm.>

Develops an iterative, space-variant, adaptive restoration algorithm incorporating both regularization and ringing suppression constraints. The algorithm is formulated using the method of projections onto convex sets (POCS). In the proposed method, regularized restoration is implemented by projecting at each iteration onto the extended Wiener solution set. Projection onto this set forces the solution to be equal to the Wiener solution at a set of frequencies over which the magnitude of the degradation transfer function is above a predetermined threshold. Ringing suppression is achieved by adaptively bounding the regional image variance in an attempt to reduce the energy of the ringing artifacts. Three corresponding convex constraint sets are defined to impose tight, moderate, and loose bounds on image variances over the uniform, texture and edge regions, respectively.>

Reconstruction from phase information via convex projections is considered for deblurring smeared images when the blurring function does not introduce any phase. The effects of initialization and constraints in the algorithm are investigated. It is shown by examples that the number of quantization levels when the smeared image is digitized determines the quality of the restored image.

A solution to the regularized signal restoration problem is formulated in the context of projections onto convex sets. An extended Wiener set is defined based on the Wiener solution in the frequency domain. A constrained Wiener solution is determined as a point in the intersection of the extended Wiener set and other deterministic constraint sets. The relationship between the proposed technique and the constrained Miller-Tikhonov regularization developed earlier is established.

The principle involved in the uniform quantization of perceived lightness is considered in the context of digital radiography. Its effects on the visibility of quantization noise (contouring artifacts) are investigated both analytically and experimentally. Several visual system models of perceived lightness as a function of stimulus luminance are used. These include a logarithmic model, various power function models, and cone models. The results can be extended for other digital imaging modalities.

The a priori information on generalized momentsm_{r}, r = 0, 1, ..., N, (m_{r} \Deltaeq \int_{\Omega} dx g_{r}(x) f(x))is shown to restrict the function f to lie in closed convex sets Cr, r = 0, 1, ..., N, in a Hilbert space setting. Therefore the a priori moment information can also be incorporated into the method of projections onto convex sets (POCS). Given the partial information (including both measurements and a priori constraints), the method of POCS reconstructs or synthesizes a feasible solution. The a priori knowledge of moments up to a finite order is assumed to synthesize a nonnegative function, consistent with a well-defined Rayleigh distribution, via the method of POCS.

In many foreseeable applications of Computerized tomography (CT) it may not be possible (e.g., because of obstructions) to obtain 360 degree view-data and reconstruction from partial view data may be unsatisfactory. In this paper we describe the method of projections onto convex sets (POCS)[1] by which the missing data can be restored from partial viewdata and a priori known properties of the original image. POCS performs significantly better than the conventional Gerchberg-Papoulis (GP) method.