# IEEE Transactions on Image Processing

## Filter Results

Displaying Results 1 - 25 of 49
• ### [Front cover]

Publication Year: 2016, Page(s): C1
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• ### IEEE Transactions on Image Processing publication information

Publication Year: 2016, Page(s): C2
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Publication Year: 2016, Page(s):3961 - 3962
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Publication Year: 2016, Page(s):3963 - 3965
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• ### [Blank page]

Publication Year: 2016, Page(s): B3966
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• ### Con-Patch: When a Patch Meets Its Context

Publication Year: 2016, Page(s):3967 - 3978
Cited by:  Papers (3)
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Measuring the similarity between the patches in images is a fundamental building block in various tasks. Naturally, the patch size has a major impact on the matching quality and on the consequent application performance. Under the assumption that our patch database is sufficiently sampled, using large patches (e.g., 21 × 21) should be preferred over small ones (e.g., 7 × 7). However,... View full abstract»

• ### Online Unmixing of Multitemporal Hyperspectral Images Accounting for Spectral Variability

Publication Year: 2016, Page(s):3979 - 3990
Cited by:  Papers (2)
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Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing a hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may vary spectrally from an image to another due to varying acquisition conditions, thus inducing possibly significant estimation errors. Against this background, the hyperspectral unmixing of s... View full abstract»

• ### Relative Forest for Visual Attribute Prediction

Publication Year: 2016, Page(s):3991 - 4003
Cited by:  Papers (1)
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Accurate prediction of the visual attributes is significant in various recognition tasks. For many visual attributes, while it is very difficult to describe the exact degrees of their presences, by comparing the pairs of samples, the relative ordering of presences may be easily figured out. Based on this observation, instead of considering such attribute as binary attribute, the relative attribute... View full abstract»

• ### Piecewise Mapping in HEVC Lossless Intra-Prediction Coding

Publication Year: 2016, Page(s):4004 - 4017
Cited by:  Papers (2)
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The lossless intra-prediction coding modality of the High Efficiency Video Coding standard provides high coding performance while allowing frame-by-frame basis access to the coded data. This is of interest in many professional applications, such as medical imaging, automotive vision, and digital preservation in libraries and archives. Various improvements to lossless intra-prediction coding have b... View full abstract»

• ### Multichannel Decoded Local Binary Patterns for Content-Based Image Retrieval

Publication Year: 2016, Page(s):4018 - 4032
Cited by:  Papers (9)
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Local binary pattern (LBP) is widely adopted for efficient image feature description and simplicity. To describe the color images, it is required to combine the LBPs from each channel of the image. The traditional way of binary combination is to simply concatenate the LBPs from each channel, but it increases the dimensionality of the pattern. In order to cope with this problem, this paper proposes... View full abstract»

• ### High-Resolution Image Classification Integrating Spectral-Spatial-Location Cues by Conditional Random Fields

Publication Year: 2016, Page(s):4033 - 4045
Cited by:  Papers (15)
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With the increase in the availability of high-resolution remote sensing imagery, classification is becoming an increasingly useful technique for providing a large area of detailed land-cover information by the use of these high-resolution images. High-resolution images have the characteristics of abundant geometric and detail information, which are beneficial to detailed classification. In order t... View full abstract»

• ### Image Coding Using Generalized Predictors Based on Sparsity and Geometric Transformations

Publication Year: 2016, Page(s):4046 - 4060
Cited by:  Papers (1)
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Directional intra prediction plays an important role in current state-of-the-art video coding standards. In directional prediction, neighbouring samples are projected along a specific direction to predict a block of samples. Ultimately, each prediction mode can be regarded as a set of very simple linear predictors, a different one for each pixel of a block. Therefore, a natural question that arise... View full abstract»

• ### Large Deformation Multiresolution Diffeomorphic Metric Mapping for Multiresolution Cortical Surfaces: A Coarse-to-Fine Approach

Publication Year: 2016, Page(s):4061 - 4074
Cited by:  Papers (2)
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Brain surface registration is an important tool for characterizing cortical anatomical variations and understanding their roles in normal cortical development and psychiatric diseases. However, surface registration remains challenging due to complicated cortical anatomy and its large differences across individuals. In this paper, we propose a fast coarse-to-fine algorithm for surface registration ... View full abstract»

• ### Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements

Publication Year: 2016, Page(s):4075 - 4090
Cited by:  Papers (4)
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Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing, and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust pr... View full abstract»

• ### SERF: A Simple, Effective, Robust, and Fast Image Super-Resolver From Cascaded Linear Regression

Publication Year: 2016, Page(s):4091 - 4102
Cited by:  Papers (8)
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Example learning-based image super-resolution techniques estimate a high-resolution image from a low-resolution input image by relying on high- and low-resolution image pairs. An important issue for these techniques is how to model the relationship between high- and low-resolution image patches: most existing complex models either generalize hard to diverse natural images or require a lot of time ... View full abstract»

• ### Video Extrapolation Method Based on Time-Varying Energy Optimization and CIP

Publication Year: 2016, Page(s):4103 - 4115
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Video extrapolation/prediction methods are often used to synthesize new videos from images. For fluid-like images and dynamic textures as well as moving rigid objects, most state-of-the-art video extrapolation methods use non-physics-based models that learn orthogonal bases from a number of images but at high computation cost. Unfortunately, data truncation can cause image degradation, i.e., blur,... View full abstract»

Publication Year: 2016, Page(s):4116 - 4128
Cited by:  Papers (2)
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Despite the previous efforts of object proposals, the detection rates of the existing approaches are still not satisfactory enough. To address this, we propose Adobe Boxes to efficiently locate the potential objects with fewer proposals, in terms of searching the object adobes that are the salient object parts easy to be perceived. Because of the visual difference between the object and its surrou... View full abstract»

• ### Blind Deblurring and Denoising of Images Corrupted by Unidirectional Object Motion Blur and Sensor Noise

Publication Year: 2016, Page(s):4129 - 4144
Cited by:  Papers (3)
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Low light photography suffers from blur and noise. In this paper, we propose a novel method to recover a dense estimate of spatially varying blur kernel as well as a denoised and deblurred image from a single noisy and object motion blurred image. A proposed method takes the advantage of the sparse representation of double discrete wavelet transform-a generative model of image blur that simplifies... View full abstract»

• ### Computationally Efficient Truncated Nuclear Norm Minimization for High Dynamic Range Imaging

Publication Year: 2016, Page(s):4145 - 4157
Cited by:  Papers (1)
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Matrix completion is a rank minimization problem to recover a low-rank data matrix from a small subset of its entries. Since the matrix rank is nonconvex and discrete, many existing approaches approximate the matrix rank as the nuclear norm. However, the truncated nuclear norm is known to be a better approximation to the matrix rank than the nuclear norm, exploiting a priori target rank informatio... View full abstract»

• ### Low-Rank Decomposition-Based Restoration of Compressed Images via Adaptive Noise Estimation

Publication Year: 2016, Page(s):4158 - 4171
Cited by:  Papers (8)
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Images coded at low bit rates in real-world applications usually suffer from significant compression noise, which significantly degrades the visual quality. Traditional denoising methods are not suitable for the content-dependent compression noise, which usually assume that noise is independent and with identical distribution. In this paper, we propose a unified framework of content-adaptive estim... View full abstract»

• ### Estimation of Gaussian, Poissonian–Gaussian, and Processed Visual Noise and Its Level Function

Publication Year: 2016, Page(s):4172 - 4185
Cited by:  Papers (1)
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We propose a method for estimating the image and video noises of different types: white Gaussian (signal-independent), mixed Poissonian-Gaussian (signal-dependent), or processed (non-white). Our method also estimates the noise level function (NLF) of these types. We do so by classifying image patches based on their intensity and variance in order to find homogeneous regions that best represent the... View full abstract»

Publication Year: 2016, Page(s):4186 - 4198
Cited by:  Papers (1)
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In this paper, we address the problem of object retrieval by hyperlinking the reference data set at subimage level. One of the main challenges in object retrieval involves small objects on cluttered backgrounds, where the similarity between the querying object and a relevant image can be heavily affected by the background. To address this problem, we propose an efficient object retrieval technique... View full abstract»

• ### Generalized Pooling for Robust Object Tracking

Publication Year: 2016, Page(s):4199 - 4208
Cited by:  Papers (7)
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Feature pooling in a majority of sparse coding-based tracking algorithms computes final feature vectors only by low-order statistics or extreme responses of sparse codes. The high-order statistics and the correlations between responses to different dictionary items are neglected. We present a more generalized feature pooling method for visual tracking by utilizing the probabilistic function to mod... View full abstract»

• ### Deep and Structured Robust Information Theoretic Learning for Image Analysis

Publication Year: 2016, Page(s):4209 - 4221
Cited by:  Papers (2)
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This paper presents a robust information theoretic (RIT) model to reduce the uncertainties, i.e., missing and noisy labels, in general discriminative data representation tasks. The fundamental pursuit of our model is to simultaneously learn a transformation function and a discriminative classifier that maximize the mutual information of data and their labels in the latent space. In this general pa... View full abstract»

• ### Multiplicative Noise and Blur Removal by Framelet Decomposition and $l_{1}$ -Based L-Curve Method

Publication Year: 2016, Page(s):4222 - 4232
Cited by:  Papers (3)
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This paper proposes a framelet-based convex optimization model for multiplicative noise and blur removal problem. The main idea is to employ framelet expansion to represent the original image and use the variable decomposition to solve the problem. Because of the nature of multiplicative noise, we decompose the observed data into the original image variable and the noise variable to obtain the res... 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