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Image Processing, IEEE Transactions on

Issue 3 • Date March 2009

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Displaying Results 1 - 25 of 27
  • Table of contents

    Page(s): C1 - C4
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    Freely Available from IEEE
  • IEEE Transactions on Image Processing publication information

    Page(s): C2
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  • Geometry-Driven Distributed Compression of the Plenoptic Function: Performance Bounds and Constructive Algorithms

    Page(s): 457 - 470
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1365 KB) |  | HTML iconHTML  

    In this paper, we study the sampling and the distributed compression of the data acquired by a camera sensor network. The effective design of these sampling and compression schemes requires, however, the understanding of the structure of the acquired data. To this end, we show that the a priori knowledge of the configuration of the camera sensor network can lead to an effective estimation of such structure and to the design of effective distributed compression algorithms. For idealized scenarios, we derive the fundamental performance bounds of a camera sensor network and clarify the connection between sampling and distributed compression. We then present a distributed compression algorithm that takes advantage of the structure of the data and that outperforms independent compression algorithms on real multiview images. View full abstract»

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  • Bayesian Image Recovery for Dendritic Structures Under Low Signal-to-Noise Conditions

    Page(s): 471 - 482
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1878 KB) |  | HTML iconHTML  

    Experimental research seeking to quantify neuronal structure constantly contends with restrictions on image resolution and variability. In particular, experimentalists often need to analyze images with very low signal-to-noise ratio (SNR). In many experiments, dye toxicity scales with the light intensity; this leads experimentalists to reduce image SNR in order to preserve the viability of the specimen. In this paper, we present a Bayesian approach for estimating the neuronal shape given low-SNR observations. This Bayesian framework has two major advantages. First, the method effectively incorporates known facts about 1) the image formation process, including blur and the Poisson nature of image noise at low intensities, and 2) dendritic shape, including the fact that dendrites are simply-connected geometric structures with smooth boundaries. Second, we may employ standard Markov chain Monte Carlo techniques for quantifying the posterior uncertainty in our estimate of the dendritic shape. We describe an efficient computational implementation of these methods and demonstrate the algorithm's performance on simulated noisy two-photon laser-scanning microscopy images. View full abstract»

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  • Rate-Distortion Efficient Piecewise Planar 3-D Scene Representation From 2-D Images

    Page(s): 483 - 494
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1128 KB) |  | HTML iconHTML  

    In any practical application of the 2-D-to-3-D conversion that involves storage and transmission, representation efficiency has an undisputable importance that is not reflected in the attention the topic received. In order to address this problem, a novel algorithm, which yields efficient 3-D representations in the rate distortion sense, is proposed. The algorithm utilizes two views of a scene to build a mesh-based representation incrementally, via adding new vertices, while minimizing a distortion measure. The experimental results indicate that, in scenes that can be approximated by planes, the proposed algorithm is superior to the dense depth map and, in some practical situations, to the block motion vector-based representations in the rate-distortion sense. View full abstract»

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  • Subjective Evaluation of Spatial Resolution and Quantization Noise Tradeoffs

    Page(s): 495 - 508
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2669 KB) |  | HTML iconHTML  

    Most full-reference fidelity/quality metrics compare the original image to a distorted image at the same resolution assuming a fixed viewing condition. However, in many applications, such as video streaming, due to the diversity of channel capacities and display devices, the viewing distance and the spatiotemporal resolution of the displayed signal may be adapted in order to optimize the perceived signal quality. For example, at low bitrate coding applications an observer may prefer to reduce the resolution or increase the viewing distance to reduce the visibility of the compression artifacts. The tradeoff between resolution/viewing conditions and visibility of compression artifacts requires new approaches for the evaluation of image quality that account for both image distortions and image size. In order to better understand such tradeoffs, we conducted subjective tests using two representative still image coders, JPEG and JPEG 2000. Our results indicate that an observer would indeed prefer a lower spatial resolution (at a fixed viewing distance) in order to reduce the visibility of the compression artifacts, but not all the way to the point where the artifacts are completely invisible. Moreover, the observer is willing to accept more artifacts as the image size decreases. The subjective test results we report can be used to select viewing conditions for coding applications. They also set the stage for the development of novel fidelity metrics. The focus of this paper is on still images, but it is expected that similar tradeoffs apply to video. View full abstract»

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  • A Fast Multilevel Algorithm for Wavelet-Regularized Image Restoration

    Page(s): 509 - 523
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1048 KB) |  | HTML iconHTML  

    We present a multilevel extension of the popular ldquothresholded Landweberrdquo algorithm for wavelet-regularized image restoration that yields an order of magnitude speed improvement over the standard fixed-scale implementation. The method is generic and targeted towards large-scale linear inverse problems, such as 3-D deconvolution microscopy. The algorithm is derived within the framework of bound optimization. The key idea is to successively update the coefficients in the various wavelet channels using fixed, subband-adapted iteration parameters (step sizes and threshold levels). The optimization problem is solved efficiently via a proper chaining of basic iteration modules. The higher level description of the algorithm is similar to that of a multigrid solver for PDEs, but there is one fundamental difference: the latter iterates though a sequence of multiresolution versions of the original problem, while, in our case, we cycle through the wavelet subspaces corresponding to the difference between successive approximations. This strategy is motivated by the special structure of the problem and the preconditioning properties of the wavelet representation. We establish that the solution of the restoration problem corresponds to a fixed point of our multilevel optimizer. We also provide experimental evidence that the improvement in convergence rate is essentially determined by the (unconstrained) linear part of the algorithm, irrespective of the type of wavelet. Finally, we illustrate the technique with some image deconvolution examples, including some real 3-D fluorescence microscopy data. View full abstract»

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  • Zero-Block Mode Decision Algorithm for H.264/AVC

    Page(s): 524 - 533
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (931 KB) |  | HTML iconHTML  

    In the previous paper , we proposed a zero-block intermode decision algorithm for H.264 video coding based upon the number of zero-blocks of 4times4 DCT coefficients between the current macroblock and the co-located macroblock. The proposed algorithm can achieve significant improvement in computation, but the computation performance is limited for high bit-rate coding. To improve computation efficiency, in this paper, we suggest an enhanced zero-block decision algorithm, which uses an early zero-block detection method to compute the number of zero-blocks instead of direct DCT and quantization (DCT/Q) calculation and incorporates two adequate decision methods into semi-stationary and nonstationary regions of a video sequence. In addition, the zero-block decision algorithm is also applied to the intramode prediction in the P frame. The enhanced zero-block decision algorithm brings out a reduction of average 27% of total encoding time compared to the zero-block decision algorithm. View full abstract»

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  • Two-Terminal Video Coding

    Page(s): 534 - 551
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2213 KB) |  | HTML iconHTML  

    Following recent works on the rate region of the quadratic Gaussian two-terminal source coding problem and limit-approaching code designs, this paper examines multiterminal source coding of two correlated, i.e., stereo, video sequences to save the sum rate over independent coding of both sequences. Two multiterminal video coding schemes are proposed. In the first scheme, the left sequence of the stereo pair is coded by H.264/AVC and used at the joint decoder to facilitate Wyner-Ziv coding of the right video sequence. The first I-frame of the right sequence is successively coded by H.264/AVC intracoding and Wyner-Ziv coding. An efficient stereo matching algorithm based on loopy belief propagation is then adopted at the decoder to produce pixel-level disparity maps between the corresponding frames of the two decoded video sequences on the fly. Based on the disparity maps, side information for both motion vectors and motion-compensated residual frames of the right sequence are generated at the decoder before Wyner-Ziv encoding. In the second scheme, source splitting is employed on top of classic and Wyner-Ziv coding for compression of both I-frames to allow flexible rate allocation between the two sequences. Experiments with both schemes on stereo video sequences using H.264/AVC, LDPC codes for Slepian-Wolf coding of the motion vectors, and scalar quantization in conjunction with LDPC codes for Wyner-Ziv coding of the residual coefficients give a slightly lower sum rate than separate H.264/AVC coding of both sequences at the same video quality. View full abstract»

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  • Low Bit-Rate Image Compression via Adaptive Down-Sampling and Constrained Least Squares Upconversion

    Page(s): 552 - 561
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6393 KB) |  | HTML iconHTML  

    Recently, many researchers started to challenge a long-standing practice of digital photography: oversampling followed by compression and pursuing more intelligent sparse sampling techniques. In this paper, we propose a practical approach of uniform down sampling in image space and yet making the sampling adaptive by spatially varying, directional low-pass prefiltering. The resulting down-sampled prefiltered image remains a conventional square sample grid, and, thus, it can be compressed and transmitted without any change to current image coding standards and systems. The decoder first decompresses the low-resolution image and then upconverts it to the original resolution in a constrained least squares restoration process, using a 2-D piecewise autoregressive model and the knowledge of directional low-pass prefiltering. The proposed compression approach of collaborative adaptive down-sampling and upconversion (CADU) outperforms JPEG 2000 in PSNR measure at low to medium bit rates and achieves superior visual quality, as well. The superior low bit-rate performance of the CADU approach seems to suggest that oversampling not only wastes hardware resources and energy, and it could be counterproductive to image quality given a tight bit budget. View full abstract»

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  • Binary Morphology With Spatially Variant Structuring Elements: Algorithm and Architecture

    Page(s): 562 - 572
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (884 KB) |  | HTML iconHTML  

    Mathematical morphology with spatially variant structuring elements outperforms translation-invariant structuring elements in various applications and has been studied in the literature over the years. However, supporting a variable structuring element shape imposes an overwhelming computational complexity, dramatically increasing with the size of the structuring element. Limiting the supported class of structuring elements to rectangles has allowed for a fast algorithm to be developed, which is efficient in terms of number of operations per pixel, has a low memory requirement, and a low latency. These properties make this algorithm useful in both software and hardware implementations, not only for spatially variant, but also translation-invariant morphology. This paper also presents a dedicated hardware architecture intended to be used as an accelerator in embedded system applications, with corresponding implementation results when targeted for both field programmable gate arrays and application specific integrated circuits. View full abstract»

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  • Pencil Back-Projection Method for SAR Imaging

    Page(s): 573 - 581
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1339 KB) |  | HTML iconHTML  

    We present a high-resolution method for spotlight mode SAR imaging that utilizes parametric modeling of projected target reflectivity density function and tomographic reconstruction. The method requires no polar-to-cartesian interpolation in spectral domain. Utilization of forward-backward total least squares bandpass matrix pencil method allows super resolution to be achieved in range for a single imaging angle. Hence, the quality of the image reconstructed by convolution back-projection is also improved. It is shown that the method is very resistant to noise and can generate images down to very low SNR values. Direct formulation in terms of physical quantities such as electric field and current density is another contribution of this paper. View full abstract»

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  • Arranging and Interpolating Sparse Unorganized Feature Points With Geodesic Circular Arc

    Page(s): 582 - 595
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3142 KB) |  | HTML iconHTML  

    A novel method to reconstruct object boundaries with geodesic circular arc is proposed in this paper. Within this framework, an energy of circular arc spline is utilized to simultaneously arrange and interpolate each member in the set of sparse unorganized feature points from the desired boundaries. A general form for a family of parametric circular arc spline is firstly derived and followed by a novel method of arranging these feature points by minimizing an energy term depending on the circular arc spline configuration defined on these feature points. With regard to the fact that the energy function is usually nonconvex and nondifferentiable at its critical points, an improved scheme of particle swarm optimizer is given to find the minimum for the energy in this paper. With this improved scheme, each pair of neighboring feature points along the boundaries of the desired objects are picked out from the set of sparse unorganized feature points, and the corresponding directional chord tangent angles are computed simultaneously to finish interpolation. We show experimentally and comparatively that the proposed method can perform effectively to restrict leakage on weak boundaries and premature convergence on long concave boundaries. Besides, it has good noise robustness and can as well extract multiple and open boundaries. View full abstract»

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  • Efficient Implementation for Spherical Flux Computation and Its Application to Vascular Segmentation

    Page(s): 596 - 612
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4983 KB) |  | HTML iconHTML  

    Spherical flux is the flux inside a spherical region, and it is very useful in the analysis of tubular structures in magnetic resonance angiography and computed tomographic angiography. The conventional approach is to estimate the spherical flux in the spatial domain. Its running time depends on the sphere radius quadratically, which leads to very slow spherical flux computation when the sphere size is large. This paper proposes a more efficient implementation for spherical flux computation in the Fourier domain. Our implementation is based on the reformulation of the spherical flux calculation using the divergence theorem, spherical step function, and the convolution operation. With this reformulation, most of the calculations are performed in the Fourier domain. We show how to select the frequency subband so that the computation accuracy can be maintained. It is experimentally demonstrated that, using the synthetic and clinical phase contrast magnetic resonance angiographic volumes, our implementation is more computationally efficient than the conventional spatial implementation. The accuracies of our implementation and that of the conventional spatial implementation are comparable. Finally, the proposed implementation can definitely benefit the computation of the multiscale spherical flux with a set of radii because, unlike the conventional spatial implementation, the time complexity of the proposed implementation does not depend on the sphere radius. View full abstract»

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  • Morphological Background Detection and Enhancement of Images With Poor Lighting

    Page(s): 613 - 623
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5722 KB) |  | HTML iconHTML  

    In this paper, some morphological transformations are used to detect the background in images characterized by poor lighting. Lately, contrast image enhancement has been carried out by the application of two operators based on the Weber's law notion. The first operator employs information from block analysis, while the second transformation utilizes the opening by reconstruction, which is employed to define the multibackground notion. The objective of contrast operators consists in normalizing the grey level of the input image with the purpose of avoiding abrupt changes in intensity among the different regions. Finally, the performance of the proposed operators is illustrated through the processing of images with different backgrounds, the majority of them with poor lighting conditions. View full abstract»

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  • Directly Manipulated Free-Form Deformation Image Registration

    Page(s): 624 - 635
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1722 KB) |  | HTML iconHTML  

    Previous contributions to both the research and open source software communities detailed a generalization of a fast scalar field fitting technique for cubic B-splines based on the work originally proposed by Lee . One advantage of our proposed generalized B-spline fitting approach is its immediate application to a class of nonrigid registration techniques frequently employed in medical image analysis. Specifically, these registration techniques fall under the rubric of free-form deformation (FFD) approaches in which the object to be registered is embedded within a B-spline object. The deformation of the B-spline object describes the transformation of the image registration solution. Representative of this class of techniques, and often cited within the relevant community, is the formulation of Rueckert who employed cubic splines with normalized mutual information to study breast deformation. Similar techniques from various groups provided incremental novelty in the form of disparate explicit regularization terms, as well as the employment of various image metrics and tailored optimization methods. For several algorithms, the underlying gradient-based optimization retained the essential characteristics of Rueckert's original contribution. The contribution which we provide in this paper is two-fold: 1) the observation that the generic FFD framework is intrinsically susceptible to problematic energy topographies and 2) that the standard gradient used in FFD image registration can be modified to a well-understood preconditioned form which substantially improves performance. This is demonstrated with theoretical discussion and comparative evaluation experimentation. View full abstract»

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  • Empirical Capacity of a Recognition Channel for Single- and Multipose Object Recognition Under the Constraint of PCA Encoding

    Page(s): 636 - 651
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1465 KB) |  | HTML iconHTML  

    The ability of practical recognition systems to recognize a large number of objects is constrained by a variety of factors that include choice of a feature extraction technique, quality of images, complexity and variability of underlying objects and of collected data. Given a feature extraction technique generating templates of objects from data and a resolution of the original images, the remaining factors can be attributed to distortions due to a recognition channel. We define the recognition channel as the environment that transforms reference templates of objects in a database into templates submitted for recognition. If templates in an object database are generated to be statistically independent and the noise in a query template is statistically independent of templates in the database, then the abilities of the recognition channel to recognize a large number of object classes can be characterized by a number called recognition capacity. In this paper, we evaluate the empirical recognition capacity of PCA-based object recognition systems. The encoded data (templates) and the additive noise in query templates are modeled to be Gaussian distributed with zero mean and estimated variances. We analyze both the case of a single encoded image and the case of encoded correlated multiple images. For this case, we propose a model that is orientation and elevation angle (pose) dependent. The fit of proposed models is judged using statistical goodness of fit tests. We define recognition rate as the ratio R=log(M)/n, where M is the number of objects to recognize and n is the length of PCA templates. The empirical capacity of PCA-based recognition systems is numerically evaluated. The empirical random coding exponent is also numerically evaluated and plotted as a function of the recognition rate. With these results, given a value of the recognition capacity and the length of templates (assume large), we can predict the number of distinct o- - bject classes that can be stored in an object library and be identified with probability of error close to zero. View full abstract»

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  • Continuous Glass Patterns for Painterly Rendering

    Page(s): 652 - 664
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6734 KB) |  | HTML iconHTML  

    Glass patterns have been exhaustively studied both in the vision literature and from a purely mathematical point of view. We extend the related formalism to the continuous case and we show that continuous Glass patterns can be used for artistic imaging applications. The general idea is to replace natural texture present in an input image with synthetic painterly texture that is generated by means of a continuous Glass pattern, whose geometrical structure is controlled by the gradient orientation of the input image. The behavior of the proposed algorithm is analytically interpreted in terms of the theory of dynamical systems. Experimental results on a broad range of input images validate the effectiveness of the proposed method in terms of lack of undesired artifacts, which are present with other existing methods, and easy interpretability of the input parameters. View full abstract»

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  • Geometry-Based Demosaicking

    Page(s): 665 - 670
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (676 KB) |  | HTML iconHTML  

    Demosaicking is a particular case of interpolation problems where, from a scalar image in which each pixel has either the red, the green or the blue component, we want to interpolate the full-color image. State-of-the-art demosaicking algorithms perform interpolation along edges, but these edges are estimated locally. We propose a level-set-based geometric method to estimate image edges, inspired by the image in-painting literature. This method has a time complexity of O(S) , where S is the number of pixels in the image, and compares favorably with the state-of-the-art algorithms both visually and in most relevant image quality measures. View full abstract»

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  • Mode-kn Factor Analysis for Image Ensembles

    Page(s): 670 - 676
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2406 KB) |  | HTML iconHTML  

    In this corespondence, we study the extra-factor estimation problem with the assumption that the training image ensemble is expressed as an nth-order tensor with the nth-dimension characterizing all features for an image and other dimensions for different extra factors, such as illuminations, poses, and identities. To overcome the local minimum issue of conventional algorithms designed for this problem, we present a novel statistical learning framework called mode-kn Factor Analysis for obtaining a closed-form solution to estimating the extra factors of any test image. In the learning stage, for the kth (k ne = n) dimension of the data tensor, the mode-kn patterns are constructed by concatenating the feature dimension and the kth extra-factor dimension, and then a mode-kn factor analysis model is learnt based on the mode- kn patterns unfolded from the original data tensor. In the inference stage, for a test image, the mode classification of the kth dimension is performed within a probabilistic framework. The advantages of mode-kn factor analysis over conventional tensor analysis algorithms are twofold: (1) a closed-form solution, instead of iterative sub-optimal solution as conventionally, is derived for estimating the extra-factor mode of any test image; and (2) the classification capability is enhanced by interacting with the process of synthesizing data of all other modes in the k th dimension. Experiments on the Pointing'04 and CMU PIE databases for pose and illumination estimation both validate the superiority of the proposed algorithm over conventional algorithms for extra-factor estimation. View full abstract»

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  • Multiresolution and Wide-Scope Depth Estimation Using a Dual-PTZ-Camera System

    Page(s): 677 - 682
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (657 KB) |  | HTML iconHTML  

    Depth information is a critical cue for scene understanding which is very important for video surveillance. Traditional depth estimation approaches based on stereo vision have been well studied in past decades. However, little research is conducted with active cameras. In this correspondence, we discuss the depth estimation problem by employing dual-PTZ (pan/tilt/zoom)-camera system. The contributions of this correspondence includes the following three aspects: 1) we analyze the effect on depth's precision with different PTZ settings; 2) we propose a coarse-to-fine framework to deal with depth estimation problem for complex region; 3) we offer a method to generate mosaic of depth maps to combine depth information from different visual angles and resolutions for large scope. These contributions will widen the usage of dual-PTZ-camera systems to a greater extent. Real-data experimental results show that the proposed approach works well. View full abstract»

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  • IEEE Transactions on Image Processing EDICS

    Page(s): 683
    Save to Project icon | Request Permissions | PDF file iconPDF (20 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Image Processing Information for authors

    Page(s): 684 - 685
    Save to Project icon | Request Permissions | PDF file iconPDF (46 KB)  
    Freely Available from IEEE
  • IEEE International Workshop on Information Forensics and Security (WIFS)

    Page(s): 686
    Save to Project icon | Request Permissions | PDF file iconPDF (589 KB)  
    Freely Available from IEEE
  • 2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

    Page(s): 687
    Save to Project icon | Request Permissions | PDF file iconPDF (522 KB)  
    Freely Available from IEEE

Aims & Scope

IEEE Transactions on Image Processing focuses on signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Scott Acton
University of Virginia
Charlottesville, VA, USA
E-mail: acton@virginia.edu 
Phone: +1 434-982-2003