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IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society

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https://www.readbyqxmd.com/read/28922121/motion-blur-kernel-estimation-via-deep-learning
#1
Xiangyu Xu, Jinshan Pan, Yu-Jin Zhang, Ming-Hsuan Yang
The success of the state-of-the-art deblurring methods mainly depends on restoration of sharp edges in a coarse-tofine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images. Motivated by the success of the existing filtering based deblurring methods, the proposed model consists of two stages: suppressing extraneous details and enhancing sharp edges. We show that the two-stage model simplifies the learning process and effectively restores sharp edges...
September 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28922120/motion-estimation-in-echocardiography-using-sparse-representation-and-dictionary-learning
#2
Nora Ouzir, Adrian Basarab, Herve Liebgott, Brahim Harbaoui, Jean-Yves Tourneret
This paper introduces a new method for cardiac motion estimation in 2D ultrasound images. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one dataset with available ground-truth, including four sequences of highly realistic simulations...
September 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28922119/pure-let-image-deconvolution
#3
Jizhou Li, Florian Luisier, Thierry Blu
We propose a non-iterative image deconvolution algorithm for data corrupted by Poisson or mixed Poisson-Gaussian noise. Many applications involve such a problem, ranging from astronomical to biological imaging. We parametrize the deconvolution process as a linear combination of elementary functions, termed as linear expansion of thresholds (LET). This parametrization is then optimized by minimizing a robust estimate of the true mean squared error, the Poisson unbiased risk estimate (PURE). Each elementary function consists of a Wiener filtering followed by a pointwise thresholding of undecimated Haar wavelet coefficients...
September 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28922118/a-fast-gradient-method-for-nonnegative-sparse-regression-with-self-dictionary
#4
Nicolas Gillis, Robert Luce
A nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them. The provably most robust methods to identify these conic basis columns are based on nonnegative sparse regression and self dictionaries, and require the solution of large-scale convex optimization problems. In this paper we study a particular nonnegative sparse regression model with self dictionary...
September 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28920900/fundamental-principles-on-learning-new-features-for-effective-dense-matching
#5
Feihu Zhang, Benjamin W Wah
In dense matching (including stereo matching and optical flow), nearly all existing approaches are based on simple features, such as gray or RGB color, gradient or simple transformations like census, to calculate matching costs. These features do not perform well in complex scenes that may involve radiometric changes, noises, overexposure and/or textureless regions. Various problems may appear, such as wrong matching at the pixel or region level, flattening/breaking of edges and/or even entire structural collapse...
September 14, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28920899/autobd-automated-bi-level-description-for-scalable-fine-grained-visual-categorization
#6
Hantao Yao, Shiliang Zhang, Chenggang Yan, Yongdong Zhang, Jintao Li, Qi Tian
Compared with traditional image classification, fine-grained visual categorization is a more challenging task because it targets to classify objects belonging to the same species, e.g., classify hundreds of birds or cars. In the past several years, researchers have made many achievements on this topic. However, most of them are heavily dependent on the artificial annotations, e.g., bounding boxes, part annotations, etc. The requirement of artificial annotations largely hinders the scalability and application...
September 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910771/a-general-framework-for-linear-distance-preserving-hashing
#7
Min Wang, Wengang Zhou, Qi Tian, Houqiang Li
Binary hashing approaches the approximate nearest neighbor search problem by transferring the data to Hamming space with explicit or implicit distance preserving constraint. With compact data representation, binary hashing identifies the approximate nearest neighbors via very efficient Hamming distance computation. In this paper, we propose a generic hashing framework with a new linear pairwise distance preserving objective and point-wise constraint. In our framework, the direct distance preserving objective aims to keep the linear relationship between the Euclidean distance and the Hamming distance of data points...
September 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910770/global-local-temporal-saliency-action-prediction
#8
Shaofan Lai, Wei-Shi Zheng, Jian-Fang Hu, Jianguo Zhang
Action prediction on a partially observed action sequence is a very challenging task. To address this challenge, we first design a global-local distance model, where a global-temporal distance compares subsequences as a whole and local-temporal distance focuses on individual segment. Our distance model introduces temporal saliency for each segment to adapt its contribution. Finally, a global-local temporal action prediction model is formulated in order to jointly learn and fuse these two types of distances...
September 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910769/single-image-shadow-removal-using-3d-intensity-surface-modeling
#9
Kai He, Rui Zhen, Jiaxing Yan, Yunfeng Ge
Shadow removal from a single image is a challenging problem, whose solution is proposed in this study using 3D intensity surface modeling. Due to the high-order textural content in the original images, a direct modeling of the intensity surface of shadow image is difficult. In this study, image decomposition technology is used as an edge-preserving filter to remove the textural detail while keeping the local-smoothness pattern of image intensity surface. Using 3D modeling, a proper intensity surface of illumination in shadow region can be obtained based on that corresponding to the same texture in the non-shadow one...
September 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910768/example-based-image-synthesis-via-randomized-patch-matching
#10
Yi Ren, Yaniv Romano, Michael Elad
Image and texture synthesis is a challenging task that has long been drawing attention in the fields of image processing, graphics, and machine learning. This problem consists of modelling the desired type of images, either through training examples or via a parametric modeling, and then generating images that belong to the same statistical origin. This work addresses the image synthesis task, focusing on two specific families of images - handwritten digits and face images. This paper offers two main contributions...
September 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910767/single-image-de-hazing-using-globally-guided-image-filtering
#11
Zhengguo Li, Jinghong Zheng
Local edge-preserving smoothing techniques such as guided image filtering (GIF) and weighted guided image filtering (WGIF) could not preserve fine structure. In this paper, a new globally guided image filtering (G-GIF) is introduced to overcome the problem. The G-GIF is composed of a global structure transfer filter and a global edge-preserving smoothing filter. The proposed filter is applied to study single image haze removal. Experimental results show that fine structure of the dehazed image is indeed preserved better by the proposed G-GIF and the dehazed images by the proposed G-GIF are sharper than those dehazed images by the existing GIF...
September 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910766/light-field-compression-with-disparity-guided-sparse-coding-based-on-structural-key-views
#12
Jie Chen, Junhui Hou, Lap-Pui Chau
Recent imaging technologies are rapidly evolving for sampling richer and more immersive representations of the 3D world. One of the emerging technologies is light field (LF) cameras based on micro-lens arrays. To record the directional information of the light rays, a much larger storage space and transmission bandwidth are required by a LF image as compared with a conventional 2D image of similar spatial dimension. Hence, the compression of LF data becomes a vital part of its application. In this paper, we propose a LF codec with disparity guided Sparse Coding over a learned perspective-shifted LF dictionary based on selected Structural Key Views (SC-SKV)...
September 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910765/multiple-semantic-matching-on-augmented-n-partite-graph-for-object-co-segmentation
#13
Chuan Wang, Hua Zhang, Liang Yang, Xiaochun Cao, Hongkai Xiong
Recent methods for object co-segmentation focus on discovering single co-occurring relation of candidate regions representing the foreground of multiple images. However, region extraction based only on low and middle level information often occupies a large area of background without the help of semantic context. In addition, seeking single matching solution very likely leads to discover local parts of common objects. To cope with these deficiencies, we present a new object cosegmentation framework, which takes advantages of semantic information and globally explores multiple co-occurring matching cliques based on an N-partite graph structure...
September 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910764/efficient-message-passing-methods-with-fully-connected-models-for-early-vision
#14
Xiao Tan, Changming Sun, Fumin Shen, Kwan-Yee K Wong
Fully connected Markov random fields and conditional random fields have recently been shown to be advantageous in many early vision tasks being formulated as multi-labeling problems, such as stereo matching and image segmentation. The maximum posterior marginal (MPM) inference method in solving fully connected models uses a hybrid framework of mean-field (MF) method and a filtering like approach, and yields excellent results. In this paper, we extend this framework in several aspects. First, we provide an alternative inference method employing a fractional belief propagation based method instead of MF...
September 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910763/feature-augmentation-for-learning-confidence-measure-in-stereo-matching
#15
Sunok Kim, Dongbo Min, Seungryong Kim, Kwanghoon Sohn
Confidence estimation is essential for refining stereo matching results through a post-processing step. This problem has recently been studied using a learning-based approach, which demonstrates a substantial improvement on conventional simple non-learning based methods. However, the formulation of learning-based methods that individually estimates the confidence of each pixel disregards spatial coherency that might exist in the confidence map, thus providing a limited performance under challenging conditions...
September 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28910762/deep-edge-guided-recurrent-residual-learning-for-image-super-resolution
#16
Wenhan Yang, Jiashi Feng, Jianchao Yang, Fang Zhao, Jiaying Liu, Zongming Guo, Shuicheng Yan
In this work, we consider the image super-resolution (SR) problem. The main challenge of image SR is to recover high-frequency details of a low-resolution (LR) image that are important for human perception. To address this essentially illposed problem, we introduce a Deep Edge Guided REcurrent rEsidual (DEGREE) network to progressively recover the highfrequency details. Different from most of existing methods that aim at predicting high-resolution (HR) images directly, DEGREE investigates an alternative route to recover the difference between a pair of LR and HR images by recurrent residual learning...
September 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28880178/adaptive-chroma-subsampling-binding-and-luma-guided-chroma-reconstruction-method-for-screen-content-images
#17
Kuo-Liang Chung, Chi-Chao Huang, Tsu-Chun Hsu
In this paper, we propose a novel adaptive chroma subsampling-binding and luma-guided (ASBLG) chroma reconstruction method for screen content images (SCIs). After receiving the decoded luma and subsampled chroma image from the decoder, a fast winner-first voting strategy is proposed to identify the used chroma subsampling scheme prior to compression. Then, the decoded luma image is subsampled as the identified subsampling scheme was performed on the chroma image such that we are able to conclude an accurate correlation between the subsampled decoded luma image and the decoded subsampled chroma image...
September 4, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28880177/hashing-with-angular-reconstructive-embeddings
#18
Mengqiu Hu, Yang Yang, Fumin Shen, Ning Xie, Heng Tao Shen
Large-scale search methods are increasingly critical for many content-based visual analysis applications, among which hashing-based approximate nearest neighbor (ANN) search techniques have attracted broad interests due to their high efficiency in storage and retrieval. However, existing hashing works are commonly designed for measuring data similarity by the Euclidean distances. In this paper, we focus on the problem of learning compact binary codes using the cosine similarity. Specifically, we proposed novel Angular Reconstructive Embeddings (ARE) method, which aims at learning binary codes by minimizing the reconstruction error between the cosine similarities computed by original features and the resulting binary embeddings...
September 4, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28880176/spectral-spatial-scale-invariant-feature-transform-for-hyperspectral-images
#19
Suhad Lateef Al-Khafaji, Jun Zhou, Ali Zia, Alan Wee-Chung Liew
Spectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral cameras. In addition, spectral condition includes images of objects with the same shape but different materials...
September 4, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28880175/hyper-lapse-from-multiple-spatially-overlapping-videos
#20
Miao Wang, Jun-Bang Liang, Song-Hai Zhang, Shao-Ping Lu, Ariel Shamir, Shi-Min Hu
Hyper-lapse video with high speed-up rate is an efficient way to overview long videos such as a human activity in first-person view. Existing hyper-lapse video creation methods produce a fast-forward video effect using only one video source. In this work, we present a novel hyper-lapse video creation approach based on multiple spatially-overlapping videos. We assume the videos share a common view or location, and find transition points where jumps from one video to another may occur. We represent the collection of videos using a hyper-lapse transition graph; the edges between nodes represent possible hyper-lapse frame transitions...
September 4, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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