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# IEEE Transactions on Image Processing

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• ### Image quality assessment: from error visibility to structural similarity

Publication Year: 2004, Page(s):600 - 612
Cited by:  Papers (7515)  |  Patents (128)
| | PDF (1726 KB) | HTML

Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative compleme... View full abstract»

• ### A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior

Publication Year: 2015, Page(s):3522 - 3533
Cited by:  Papers (37)
| | PDF (2997 KB) | HTML

Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we propose a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth informatio... View full abstract»

• ### Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Publication Year: 2017, Page(s):3142 - 3155
| | PDF (5828 KB) | HTML

The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image de... View full abstract»

• ### Shapes From Pixels

Publication Year: 2016, Page(s):1193 - 1206
Cited by:  Papers (1)
| | PDF (3037 KB) | HTML

Continuous-domain visual signals are usually captured as discrete (digital) images. This operation is not invertible in general, in the sense that the continuous-domain signal cannot be exactly reconstructed based on the discrete image, unless it satisfies certain constraints (e.g., bandlimitedness). In this paper, we study the problem of recovering shape images with smooth boundaries from a set o... View full abstract»

• ### Image Super-Resolution Via Sparse Representation

Publication Year: 2010, Page(s):2861 - 2873
Cited by:  Papers (1086)  |  Patents (11)
| | PDF (1802 KB) | HTML

This paper presents a new approach to single-image superresolution, based upon sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and th... View full abstract»

• ### Facial Age Estimation With Age Difference

Publication Year: 2017, Page(s):3087 - 3097
| | PDF (2636 KB) | HTML

Age estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the immensely unlabeled or weakly labeled training data, e.g., the huge amount of h... View full abstract»

• ### Perceptual Image Fusion Using Wavelets

Publication Year: 2017, Page(s):1076 - 1088
| | PDF (7225 KB) | HTML

A perceptual image fusion method is proposed that employs explicit luminance and contrast masking models. These models are combined to give the perceptual importance of each coefficient produced by the dual-tree complex wavelet transform of each input image. This combined model of perceptual importance is used to select which coefficients are retained and furthermore to determine how to present th... View full abstract»

• ### A Decomposition Framework for Image Denoising Algorithms

Publication Year: 2016, Page(s):388 - 399
Cited by:  Papers (10)
| | PDF (3720 KB) | HTML

In this paper, we consider an image decomposition model that provides a novel framework for image denoising. The model computes the components of the image to be processed in a moving frame that encodes its local geometry (directions of gradients and level lines). Then, the strategy we develop is to denoise the components of the image in the moving frame in order to preserve its local geometry, wh... View full abstract»

• ### ViBe: A Universal Background Subtraction Algorithm for Video Sequences

Publication Year: 2011, Page(s):1709 - 1724
Cited by:  Papers (428)  |  Patents (3)
| | PDF (2571 KB) | HTML

This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing rando... View full abstract»

• ### No-Reference Image Quality Assessment in the Spatial Domain

Publication Year: 2012, Page(s):4695 - 4708
Cited by:  Papers (418)  |  Patents (1)
| | PDF (1817 KB) | HTML

We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coeffic... View full abstract»

• ### Fast Unsupervised Bayesian Image Segmentation With Adaptive Spatial Regularisation

Publication Year: 2017, Page(s):2577 - 2587
| | PDF (3637 KB) | HTML

This paper presents a new Bayesian estimation technique for hidden Potts-Markov random fields with unknown regularisation parameters, with application to fast unsupervised K-class image segmentation. The technique is derived by first removing the regularisation parameter from the Bayesian model by marginalisation, followed by a small-variance-asymptotic (SVA) analysis in which the spatial regulari... View full abstract»

• ### Learning Multilayer Channel Features for Pedestrian Detection

Publication Year: 2017, Page(s):3210 - 3220
| | PDF (2321 KB) | HTML

Pedestrian detection based on the combination of convolutional neural network (CNN) and traditional handcrafted features (i.e., HOG+LUV) has achieved great success. In general, HOG+LUV are used to generate the candidate proposals and then CNN classifies these proposals. Despite its success, there is still room for improvement. For example, CNN classifies these proposals by the fully ... View full abstract»

• ### Curvature Filters Efficiently Reduce Certain Variational Energies

Publication Year: 2017, Page(s):1786 - 1798
| | PDF (7552 KB) | HTML

In image processing, the rapid approximate solution of variational problems involving generic data-fitting terms is often of practical relevance, for example in real-time applications. Variational solvers based on diffusion schemes or the Euler-Lagrange equations are too slow and restricted in the types of data-fitting terms. Here, we present a filter-based approach to reduce variational energies ... View full abstract»

• ### Active contours without edges

Publication Year: 2001, Page(s):266 - 277
Cited by:  Papers (4004)  |  Patents (59)
| | PDF (508 KB) | HTML

We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, t... View full abstract»

• ### Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

Publication Year: 2006, Page(s):3736 - 3745
Cited by:  Papers (1682)  |  Patents (14)
| | PDF (3758 KB) | HTML

We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself,... View full abstract»

• ### Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

Publication Year: 2007, Page(s):2080 - 2095
Cited by:  Papers (1537)  |  Patents (43)
| | PDF (6170 KB) | HTML

We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call "groups." Collaborative Altering is a special procedure developed to deal with these 3D groups. We realize it using the three successive steps: 3D transformatio... View full abstract»

• ### Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal

Publication Year: 2017, Page(s):3098 - 3112
| | PDF (6481 KB) | HTML

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true i... View full abstract»

• ### Salient Object Detection: A Benchmark

Publication Year: 2015, Page(s):5706 - 5722
Cited by:  Papers (81)
| | PDF (8912 KB) | HTML

We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven challenging data sets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms... View full abstract»

• ### $L_{0}$ Gradient Projection

Publication Year: 2017, Page(s):1554 - 1564
| | PDF (4260 KB) | HTML

Minimizing L0 gradient, the number of the non-zero gradients of an image, together with a quadratic data-fidelity to an input image has been recognized as a powerful edge-preserving filtering method. However, the L0 gradient minimization has an inherent difficulty: a user-given parameter controlling the degree of flatness does not have a physical meaning since the parameter j... View full abstract»

• ### Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions

Publication Year: 2010, Page(s):1635 - 1650
Cited by:  Papers (618)  |  Patents (4)
| | PDF (5778 KB) | HTML

Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, kernel-based feature extraction and multiple feature fusion. Specifically, we make three ma... View full abstract»

• ### Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes

Publication Year: 2017, Page(s):1992 - 2004
| | PDF (2569 KB) | HTML

This paper proposes a fast and reliable method for anomaly detection and localization in video data showing crowded scenes. Time-efficient anomaly localization is an ongoing challenge and subject of this paper. We propose a cubic-patch-based method, characterised by a cascade of classifiers, which makes use of an advanced feature-learning approach. Our cascade of classifiers has two main stages. F... View full abstract»

• ### Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework

Publication Year: 2017, Page(s):2988 - 3001
| | PDF (2797 KB) | HTML

Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State-of-the-art approaches for solving this problem follow a two-stage approach. In the first step, an affinity matrix is learned from the data using sparse or low-rank minimization techniques. In the second step, the segmentation is found by applying spectral clustering to this affinity. While this appr... View full abstract»

• ### FSIM: A Feature Similarity Index for Image Quality Assessment

Publication Year: 2011, Page(s):2378 - 2386
Cited by:  Papers (674)
| | PDF (1257 KB) | HTML

Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly accord... View full abstract»

• ### Affine Non-Local Means Image Denoising

Publication Year: 2017, Page(s):2137 - 2148
| | PDF (7901 KB) | HTML

This paper presents an extension of the Non-Local Means denoising method, that effectively exploits the affine invariant self-similarities present in the images of real scenes. Our method provides a better image denoising result by grounding on the fact that in many occasions similar patches exist in the image but have undergone a transformation. The proposal uses an affine invariant patch similar... View full abstract»

• ### Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-Rank Approximation

Publication Year: 2017, Page(s):3171 - 3186
| | PDF (7591 KB) | HTML

Recovering the image corrupted by additive white Gaussian noise (AWGN) and impulse noise is a challenging problem due to its difficulties in an accurate modeling of the distributions of the mixture noise. Many efforts have been made to first detect the locations of the impulse noise and then recover the clean image with image in painting techniques from an incomplete image corrupted by AWGN. Howev... View full abstract»

• ### Style Transfer Via Texture Synthesis

Publication Year: 2017, Page(s):2338 - 2351
| | PDF (12164 KB) | HTML

Style transfer is a process of migrating a style from a given image to the content of another, synthesizing a new image, which is an artistic mixture of the two. Recent work on this problem adopting convolutional neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained. There exists an alternative path toward handling the style transfer task, via ... View full abstract»

• ### Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal

Publication Year: 2017, Page(s):2944 - 2956
| | PDF (10862 KB) | HTML

We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between rainy and clean image detail layers from data. Because we do not possess the ground truth corresponding to real-world rainy images, we synthesize images with rain for training. In contrast to othe... View full abstract»

• ### Video Saliency Detection via Spatial-Temporal Fusion and Low-Rank Coherency Diffusion

Publication Year: 2017, Page(s):3156 - 3170
| | PDF (4338 KB) | HTML Media

This paper advocates a novel video saliency detection method based on the spatial-temporal saliency fusion and low-rank coherency guided saliency diffusion. In sharp contrast to the conventional methods, which conduct saliency detection locally in a frame-by-frame way and could easily give rise to incorrect low-level saliency map, in order to overcome the existing difficulties, this paper proposes... View full abstract»

• ### Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification

Publication Year: 2017, Page(s):3449 - 3462
| | PDF (3527 KB) | HTML

A linear synthesis model-based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it, however, suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Ad... View full abstract»

• ### Re-Weighted Discriminatively Embedded $K$ -Means for Multi-View Clustering

Publication Year: 2017, Page(s):3016 - 3027
| | PDF (3000 KB) | HTML

Recent years, more and more multi-view data are widely used in many real-world applications. This kind of data (such as image data) is high dimensional and obtained from different feature extractors, which represents distinct perspectives of the data. How to cluster such data efficiently is a challenge. In this paper, we propose a novel multi-view clustering framework, called re-weighted discrimin... View full abstract»

• ### Region filling and object removal by exemplar-based image inpainting

Publication Year: 2004, Page(s):1200 - 1212
Cited by:  Papers (927)  |  Patents (48)
| | PDF (5494 KB) | HTML

A new algorithm is proposed for removing large objects from digital images. The challenge is to fill in the hole that is left behind in a visually plausible way. In the past, this problem has been addressed by two classes of algorithms: 1) "texture synthesis" algorithms for generating large image regions from sample textures and 2) "inpainting" techniques for filling in small image gaps. The forme... View full abstract»

• ### Linear Spectral Clustering Superpixel

Publication Year: 2017, Page(s):3317 - 3330
| | PDF (5848 KB) | HTML

In this paper, we present a superpixel segmentation algorithm called linear spectral clustering (LSC), which is capable of producing superpixels with both high boundary adherence and visual compactness for natural images with low computational costs. In LSC, a normalized cuts-based formulation of image segmentation is adopted using a distance metric that measures both the color similarity and the ... View full abstract»

• ### Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images

Publication Year: 2017, Page(s):2918 - 2928
| | PDF (1802 KB) | HTML Media Code

In hyperspectral image analysis, the classification task has generally been addressed jointly with dimensionality reduction due to both the high correlation between the spectral features and the noise present in spectral bands, which might significantly degrade classification performance. In supervised classification, limited training instances in proportion with the number of spectral features ha... View full abstract»

• ### Single Image Super-Resolution Using Gaussian Process Regression With Dictionary-Based Sampling and Student- ${t}$ Likelihood

Publication Year: 2017, Page(s):3556 - 3568
| | PDF (4593 KB) | HTML

Gaussian process regression (GPR) is an effective statistical learning method for modeling non-linear mapping from an observed space to an expected latent space. When applying it to example learning-based super-resolution (SR), two outstanding issues remain. One is its high computational complexity restricts SR application when a large data set is available for learning task. The other is that the... View full abstract»

• ### Robust ImageGraph: Rank-Level Feature Fusion for Image Search

Publication Year: 2017, Page(s):3128 - 3141
| | PDF (6202 KB) | HTML

Recently, feature fusion has demonstrated its effectiveness in image search. However, bad features and inappropriate parameters usually bring about false positive images, i.e., outliers, leading to inferior performance. Therefore, a major challenge of fusion scheme is how to be robust to outliers. Towards this goal, this paper proposes a rank-level framework for robust feature fus... View full abstract»

• ### Low-Rank Embedding for Robust Image Feature Extraction

Publication Year: 2017, Page(s):2905 - 2917
| | PDF (2491 KB) | HTML

Robustness to noises, outliers, and corruptions is an important issue in linear dimensionality reduction. Since the sample-specific corruptions and outliers exist, the class-special structure or the local geometric structure is destroyed, and thus, many existing methods, including the popular manifold learning- based linear dimensionality methods, fail to achieve good performance in recognition ta... View full abstract»

• ### Universal Multimode Background Subtraction

Publication Year: 2017, Page(s):3249 - 3260
| | PDF (2184 KB) | HTML

In this paper, we present a complete change detection system named multimode background subtraction. The universal nature of system allows it to robustly handle multitude of challenges associated with video change detection, such as illumination changes, dynamic background, camera jitter, and moving camera. The system comprises multiple innovative mechanisms in background modeling, model update, p... View full abstract»

• ### PCANet: A Simple Deep Learning Baseline for Image Classification?

Publication Year: 2015, Page(s):5017 - 5032
Cited by:  Papers (46)
| | PDF (4347 KB) | HTML

In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing a... View full abstract»

• ### LIME: Low-Light Image Enhancement via Illumination Map Estimation

Publication Year: 2017, Page(s):982 - 993
| | PDF (9437 KB) | HTML

When one captures images in low-light conditions, the images often suffer from low visibility. Besides degrading the visual aesthetics of images, this poor quality may also significantly degenerate the performance of many computer vision and multimedia algorithms that are primarily designed for high-quality inputs. In this paper, we propose a simple yet effective low-light image enhancement (LIME)... View full abstract»

• ### DehazeNet: An End-to-End System for Single Image Haze Removal

Publication Year: 2016, Page(s):5187 - 5198
Cited by:  Papers (4)
| | PDF (5832 KB) | HTML

Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and ou... View full abstract»

• ### Discriminative Multi-View Interactive Image Re-Ranking

Publication Year: 2017, Page(s):3113 - 3127
| | PDF (3670 KB) | HTML

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users’ intentions and multiple features that sufficiently describe ... View full abstract»

• ### Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

Publication Year: 2017, Page(s):1979 - 1991
| | PDF (3800 KB) | HTML

Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method ... View full abstract»

• ### Zero-Shot Learning With Transferred Samples

Publication Year: 2017, Page(s):3277 - 3290
| | PDF (3494 KB) | HTML

By transferring knowledge from the abundant labeled samples of known source classes, zero-shot learning (ZSL) makes it possible to train recognition models for novel target classes that have no labeled samples. Conventional ZSL approaches usually adopt a two-step recognition strategy, in which the test sample is projected into an intermediary space in the first step, and then the recognition is ca... View full abstract»

• ### A Robust and Efficient Approach to License Plate Detection

Publication Year: 2017, Page(s):1102 - 1114
| | PDF (4247 KB) | HTML

This paper presents a robust and efficient method for license plate detection with the purpose of accurately localizing vehicle license plates from complex scenes in real time. A simple yet effective image downscaling method is first proposed to substantially accelerate license plate localization without sacrificing detection performance compared with that achieved using the original image. Furthe... View full abstract»

• ### A Completed Modeling of Local Binary Pattern Operator for Texture Classification

Publication Year: 2010, Page(s):1657 - 1663
Cited by:  Papers (470)
| | PDF (304 KB) | HTML

In this correspondence, a completed modeling of the local binary pattern (LBP) operator is proposed and an associated completed LBP (CLBP) scheme is developed for texture classification. A local region is represented by its center pixel and a local difference sign-magnitude transform (LDSMT). The center pixels represent the image gray level and they are converted into a binary code, namely CLBP-Ce... View full abstract»

• ### No-Reference Quality Assessment of Tone-Mapped HDR Pictures

Publication Year: 2017, Page(s):2957 - 2971
| | PDF (8500 KB) | HTML

Being able to automatically predict digital picture quality, as perceived by human observers, has become important in many applications where humans are the ultimate consumers of displayed visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, which are usually created from multiple exposures of the same scene, can provide 16 or 32 b/color... View full abstract»

• ### Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

Publication Year: 2011, Page(s):1838 - 1857
Cited by:  Papers (388)  |  Patents (1)
| | PDF (5031 KB) | HTML

As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of the l1-norm optimization techniques and the fact that natural images are intrinsically sparse in some domains. The image restoration quality largely depends on whether the em... View full abstract»

• ### Deep Label Distribution Learning With Label Ambiguity

Publication Year: 2017, Page(s):2825 - 2838
| | PDF (4760 KB) | HTML

Convolutional neural networks (ConvNets) have achieved excellent recognition performance in various visual recognition tasks. A large labeled training set is one of the most important factors for its success. However, it is difficult to collect sufficient training images with precise labels in some domains, such as apparent age estimation, head pose estimation, multilabel classification, and seman... View full abstract»

• ### Learning Multi-Instance Deep Discriminative Patterns for Image Classification

Publication Year: 2017, Page(s):3385 - 3396
| | PDF (3054 KB) | HTML

Finding an effective and efficient representation is very important for image classification. The most common approach is to extract a set of local descriptors, and then aggregate them into a high-dimensional, more semantic feature vector, like unsupervised bag-of-features and weakly supervised part-based models. The latter one is usually more discriminative than the former due to the use of infor... View full abstract»

• ### Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval

Publication Year: 2017, Page(s):2868 - 2881
| | PDF (3802 KB) | HTML

Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even w... View full abstract»

## 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