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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/30418909/image-inpainting-using-nonlocal-texture-matching-and-nonlinear-filtering
#1
Ding Ding, Sundaresh Ram, Jeffrey J Rodriguez
Nonlocal texture similarity and local intensity smoothness are both essential for solving most image inpainting problems. In this paper, we propose a novel image inpainting algorithm that is capable of reproducing the underlying textural details using a nonlocal texture measure and also smoothing pixel intensity seamlessly in order to achieve natural-looking inpainted images. For matching texture, we propose a Gaussian-weighted nonlocal texture similarity measure to obtain multiple candidate patches for each target patch...
November 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30418908/a-novel-scheme-based-on-the-diffusion-to-edge-detection
#2
Yuesheng He, Lionel M Ni
A novel scheme of edge detection based on the physical law of diffusion is presented in this paper. Though the most current researches are using data based methods such as Deep Neural Networks, these methods on machine learning need big data of labeled ground truth as well as a large amount of resources for training. On the other hand, the widely used traditional methods are based on the gradient of the grayscale or color of images with using different sorts of mathematical tools to accomplish the mission. Instead of treating the outline of an object in an image as a kind of gradient of grayscale or color, our scheme deals with the edge detection as a character of an energy diffusing in the space of media such as Charge-coupled Device (CCD)...
November 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30418907/a-convex-optimization-framework-for-video-quality-and-resolution-enhancement-from-multiple-descriptions
#3
Andrei Purica, Benoit Boyadjis, Beatrice Pesquet-Popescu, Frederic Dufaux, Cyril Bergeron
Transmission and compression technologies advancement over the past decade led to a shift of multimedia content towards cloud systems. Multiple copies of the same video are available through numerous distribution systems. Different compression levels, algorithms and resolutions are used to match the requirements of particular applications. As 4k display technologies are rapidly adopted, resolution enhancement algorithms are of vital importance. Current solutions do not take into account the particularities of different video encoders, while video reconstruction methods from compressed sources do not provide resolution enhancement...
November 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30418906/fastderain-a-novel-video-rain-streak-removal-method-using-directional-gradient-priors
#4
Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng, Yao Wang
Rain streaks removal is an important issue in outdoor vision systems and has recently been investigated extensively. In this paper, we propose a novel video rain streak removal approach FastDeRain, which fully considers the discriminative characteristics of rain streaks and the clean video in the gradient domain. Specifically, on the one hand, rain streaks are sparse and smooth along the direction of the raindrops, whereas on the other hand, clean videos exhibit piecewise smoothness along the rain-perpendicular direction and continuity along the temporal direction...
November 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30418905/robust-reflection-removal-based-on-light-field-imaging
#5
Tingtian Li, Daniel P K Lun, Y H Chan, Budianto
In daily photography, it is common to capture images in the reflection of an unwanted scene. This circumstance arises frequently when imaging through a semi-reflecting material such as glass. The unwanted reflection will affect the visibility of the background image and introduce ambiguity that perturbs the subsequent analysis on the image. It is a very challenging task to remove the reflection of an image since the problem is severely ill-posed. In this paper, we propose a novel algorithm to solve the reflection removal problem based on light field (LF) imaging...
November 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30418904/vrsa-net-vr-sickness-assessment-considering-exceptional-motion-for-360-degree-vr-video
#6
Hak Gu Kim, Heoun-Taek Lim, Sangmin Lee, Yong Man Ro
The viewing safety is one of the main issues in viewing virtual reality (VR) content. In particular, VR sickness could occur when watching immersive VR content. To deal with the viewing safety for VR content, objective assessment of VR sickness is of great importance. In this paper, we propose a novel objective VR sickness assessment (VRSA) network based on deep generative model for automatically predicting the VR sickness score. The proposed method takes into account motion patterns of VR videos in which an exceptional motion is a critical factor inducing excessive VR sickness in human motion perception...
November 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30418903/a-second-order-multi-stencil-fast-marching-method-with-a-non-constant-local-cost-model
#7
Susana Merino-Caviedes, Lucilio Cordero-Grande, Maria Teresa Perez, Pablo Casaseca-de-la-Higuera, Marcos Martin-Fernandez, Rachid Deriche, Carlos Alberola-Lopez
The Fast Marching method is widely employed in several fields of image processing. Some years ago a Multi-Stencil version (MSFM) was introduced to improve its accuracy by solving the equation for a set of stencils and choosing the best solution at each considered node. The following work proposes a modified numerical scheme for MSFM to take into account the variation of the local cost, which has proven to be second order. The influence of the stencil set choice on the algorithm outcome with respect to stencil orthogonality and axis swapping is also explored, where stencils are taken from neighborhoods of varying radius...
November 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30418902/single-image-reflection-removal-using-convolutional-neural-networks
#8
Yakun Chang, Cheolkon Jung
When people take a picture through glass, the scene behind the glass is often interfered by specular reflection. Due to relatively easy implementation, most studies have tried to recover the transmitted scene from multiple images rather than single image. However, the use of multiple images is not practical for common users in real situations due to the critical shooting conditions. In this paper, we propose single image reflection removal using convolutional neural networks. We provide a ghosting model that causes reflection effects in captured images...
November 9, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30418901/an-augmented-linear-mixing-model-to-address-spectral-variability-for-hyperspectral-unmixing
#9
Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu
Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing...
November 9, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30403631/deep-visual-saliency-on-stereoscopic-images
#10
Anh-Duc Nguyen, Jongyoo Kim, Heeseok Oh, Haksub Kim, Weisi Lin, Sanghoon Lee
Visual saliency on stereoscopic 3D (S3D) images has been shown to be heavily influenced by image quality. Hence, this dependency is an important factor in image quality prediction, image restoration and discomfort reduction, but it is still very difficult to predict such a nonlinear relation in images. In addition, most algorithms specialized in detecting visual saliency on pristine images may unsurprisingly fail when facing distorted images. In this paper, we investigate a deep learning scheme named Deep Visual Saliency (DeepVS) to achieve a more accurate and reliable saliency predictor even in the presence of distortions...
November 2, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30403630/synthetic-data-generation-for-end-to-end-thermal-infrared-tracking
#11
Lichao Zhang, Abel Gonzalez-Garcia, Joost van de Weijer, Martin Danelljan, Fahad Shahbaz Khan
The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks for tracking in thermal infrared (TIR) images. Therefore, most state of the art methods on tracking for TIR data are still based on hand-crafted features. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the abundantly available labeled RGB data to synthetic TIR data...
November 2, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30387733/towards-achieving-robust-low-level-and-high-level-scene-parsing
#12
Bing Shuai, Henghui Ding, Ting Liu, Gang Wang, Xudong Jiang
In this paper, we address the challenging task of scene segmentation. We first discuss and compare two widely used approaches to retain detailed spatial information from pretrained CNN - "dilation" and "skip". Then, we demonstrate that the parsing performance of "skip" network can be noticeably improved by modifying the parameterization of skip layers. Furthermore, we introduce a "dense skip" architecture to retain a rich set of low-level information from pre-trained CNN, which is essential to improve the low-level parsing performance...
October 31, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30387732/cycle-consistent-deep-generative-hashing-for-cross-modal-retrieval
#13
Lin Wu, Yang Wang, Ling Shao
In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to learn a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship. To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further delved into the adversarial training to strengthen the correlation between the inputs and corresponding outputs...
October 31, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30387731/deepcrack-learning-hierarchical-convolutional-features-for-crack-detection
#14
Qin Zou, Zheng Zhang, Qingquan Li, Xianbiao Qi, Qian Wang, Song Wang
Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which brings great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack - an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep convolutional features learned at hierarchical convolutional stages are fused together to capture the line structures...
October 31, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30387730/gib-a-game-theory-inspired-binarization-technique-for-degraded-document-images
#15
Showmik Bhowmik, Ram Sarkar, Bishwadeep Das, David Doermann
Document image binarization classifies each pixel in an input document image as either foreground or background under the assumption that the document is pseudo binary in nature. However, noise introduced during acquisition or due to aging or handling of the document can make binarization a challenging task. This paper presents a novel game theory inspired binarization technique for degraded document images. A two-player, non-zero-sum, non-cooperative game is designed at the pixel level to extract the local information, which is then fed to a K-means algorithm to classify a pixel as foreground or background...
October 31, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30387729/cross-modal-attentional-context-learning-for-rgb-d-object-detection
#16
Guanbin Li, Yukang Gan, Hejun Wu, Nong Xiao, Liang Lin
Recognizing objects from simultaneously sensed photometric (RGB) and depth channels is a fundamental yet practical problem in many machine vision applications such as robot grasping and autonomous driving. In this paper, we address this problem by developing a Cross-Modal Attentional Context (CMAC) learning framework, which enables the full exploitation of the context information from both RGB and depth data. Compared to existing RGB-D object detection frameworks, our approach has several appealing properties...
October 31, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30387728/fast-high-dimensional-bilateral-and-nonlocal-means-filtering
#17
Pravin Nair, Kunal N Chaudhury
Existing fast algorithms for bilateral and nonlocal means filtering mostly work with grayscale images. They cannot easily be extended to high-dimensional data such as color and hyperspectral images, patch-based data, flow-fields, etc. In this paper, we propose a fast algorithm for high-dimensional bilateral and nonlocal means filtering. Unlike existing approaches, where the focus is on approximating the data (using quantization) or the filter kernel (via analytic expansions), we locally approximate the kernel using weighted and shifted copies of a Gaussian, where the weights and shifts are inferred from the data...
October 31, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30387727/modified-quality-threshold-clustering-for-temporal-analysis-and-classification-of-lung-lesions
#18
Stelmo Magalhaes Barros Netto, Aristofanes Correa Silva, Anselmo Cardoso de Paiva, Rofolfo Acatauassu Nunes, Marcelo Gattass
Lung cancer is the type of cancer that most kills after the beginning diagnostic. To aid the specialist in this diagnostic, the temporal evaluation comes up as a tool to analyze the indeterminate lesions that may be benign or malignant lesions during the treatment. With this intention, it is proposed a methodology for analysis, quantification, and visualization of changes in lung lesions. This methodology used modified quality threshold clustering algorithm to associate each voxel of the lesion to a cluster and the changes in the lesion over time are defined when a voxel changes to another cluster...
October 31, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30371373/video-person-re-identification-by-temporal-residual-learning
#19
Ju Dai, Pingping Zhang, Dong Wang, Huchuan Lu, Hongyu Wang
In this paper, we propose a novel feature learning framework for video person re-identification (re-ID). The proposed framework largely aims to exploit the adequate temporal information of video sequences and tackle the poor spatial alignment of moving pedestrians. More specifically, for exploiting the temporal information, we design a temporal residual learning (TRL) module to simultaneously extract the generic and specific features of consecutive frames. The TRL module is equipped with two bi-directional LSTM (BiLSTM), which are respectively responsible to describe a moving person in different aspects, providing complementary information for better feature representations...
October 29, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30371366/an-adaptive-markov-random-field-for-structured-compressive-sensing
#20
Suwichaya Suwanwimolkul, Lei Zhang, Dong Gong, Zhen Zhang, Chao Chen, Damith C Ranasinghe, Qinfeng Shi
Exploiting intrinsic structures in sparse signals underpins the recent progress in compressive sensing (CS). The key for exploiting such structures is to achieve two desirable properties: generality (i.e., the ability to fit a wide range of signals with diverse structures) and adaptability (i.e., being adaptive to a specific signal). Most existing approaches, however, often only achieve one of these two properties. In this study, we propose a novel adaptive Markov random field sparsity prior for CS, which not only is able to capture a broad range of sparsity structures, but also can adapt to each sparse signal through refining the parameters of the sparsity prior with respect to the compressed measurements...
October 29, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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