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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/29733271/denoising-of-microscopy-images-a-review-of-the-state-of-the-art-and-a-new-sparsity-based-method
#1
William Meiniel, Jean-Christophe Olivo-Marin, Elsa D Angelini
This paper reviews the state-of-the-art in denoising methods for biological microscopy images and introduces a new and original sparsity-based algorithm. The proposed method combines total variation (TV) spatial regularization, enhancement of low-frequency information, and aggregation of sparse estimators and is able to handle simple and complex types of noise (Gaussian, Poisson, and mixed), without any a priori model and with a single set of parameter values. An extended comparison is also presented, that evaluates the denoising performance of the thirteen (including ours) state-of-the-art denoising methods specifically designed to handle the different types of noises found in bioimaging...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29727272/constrained-optimization-for-plane-based-stereo
#2
Shahnawaz Ahmed, Miles Hansard, Andrea Cavallaro
Depth and surface normal estimation are crucial components in understanding 3D scene geometry from calibrated stereo images. In this paper, we propose visibility and disparity magnitude constraints for slanted patches in the scene. These constraints can be used to associate geometrically feasible planes with each point in the disparity space. The new constraints are validated in the PatchMatch Stereo framework. We use these new constraints not only for initialization, but also in the local plane refinement step of this iterative algorithm...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29727271/structure-aware-local-sparse-coding-for-visual-tracking
#3
Yuankai Qi, Lei Qin, Jian Zhang, Shengping Zhang, Qingming Huang, Ming-Hsuan Yang
Sparse coding has been applied to visual tracking and related vision problems with demonstrated success in recent years. Existing tracking methods based on local sparse coding sample patches from a target candidate and sparsely encode these using a dictionary consisting of patches sampled from target template images. The discriminative strength of existing methods based on local sparse coding is limited as spatial structure constraints among the template patches are not exploited. To address this problem, we propose a structure-aware local sparse coding algorithm, which encodes a target candidate using templates with both global and local sparsity constraints...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29698212/residual-highway-convolutional-neural-networks-for-in-loop-filtering-in-hevc
#4
Yongbing Zhang, Tao Shen, Xiangyang Ji, Yun Zhang, Ruiqin Xiong, Qionghai Dai
High efficiency video coding (HEVC) standard achieves half bit-rate reduction while keeping the same quality compared with AVC. However, it still cannot satisfy the demand of higher quality in real applications, especially at low bit rates. To further improve the quality of reconstructed frame while reducing the bitrates, a residual highway convolutional neural network (RHCNN) is proposed in this paper for in-loop filtering in HEVC. The RHCNN is composed of several residual highway units and convolutional layers...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29698211/high-order-model-and-dynamic-filtering-for-frame-rate-up-conversion
#5
Wenbo Bao, Xiaoyun Zhang, Li Chen, Lianghui Ding, Zhiyong Gao
This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29698210/video-synopsis-in-complex-situations
#6
Xuelong Li, Zhigang Wang, Xiaoqiang Lu
Video synopsis is an effective technique for surveillance video browsing and storage. However, most of the existing video synopsis approaches are not suitable for complex situations, especially crowded scenes. This is because these approaches heavily depend on the preprocessing results of foreground segmentation and multiple objects tracking, but the preprocessing techniques usually achieve poor performance in crowded scenes. To address this problem, we propose a comprehensive video synopsis approach which can be applied to scenes with drastically varying crowdedness...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29698209/single-image-super-resolution-based-on-rational-fractal-interpolation
#7
Yunfeng Zhang, Qinglan Fan, Fangxun Bao, Yifang Liu, Caiming Zhang
This paper presents a novel single-image super-resolution (SR) procedure, which upscales a given low-resolution (LR) input image to a high-resolution image while preserving the textural and structural information. First, we construct a new type of bivariate rational fractal interpolation model and investigate its analytical properties. This model has different forms of expression with various values of the scaling factors and shape parameters; thus, it can be employed to better describe image features than current interpolation schemes...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29698208/toward-the-repeatability-and-robustness-of-the-local-reference-frame-for-3d-shape-matching-an-evaluation
#8
Jiaqi Yang, Yang Xiao, Zhiguo Cao
The local reference frame (LRF), as an independent coordinate system constructed on the local 3D surface, is broadly employed in 3D local feature descriptors. The benefits of the LRF include rotational invariance and full 3D spatial information, thereby greatly boosting the distinctiveness of a 3D feature descriptor. There are numerous LRF methods in the literature; however, no comprehensive study comparing their repeatability and robustness performance under different application scenarios and nuisances has been conducted...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29698207/globally-variance-constrained-sparse-representation-and-its-application-in-image-set-coding
#9
Xiang Zhang, Jiarui Sun, Siwei Ma, Zhouchen Lin, Jian Zhang, Shiqi Wang, Wen Gao
Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary based on which various successful applications have been achieved. However, in the scenario of data compression, its efficiency and popularity are hindered. It is because of the fact that encoding sparsely distributed coefficients may consume more bits for representing the index of nonzero coefficients. Therefore, introducing an accurate rate constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29698206/pamm-pose-aware-multi-shot-matching-for-improving-person-re-identification
#10
Yeong-Jun Cho, Kuk-Jin Yoon
Person re-identification is the problem of recognizing people across different images or videos with non-overlapping views. Although a significant progress has been made in person re-identification over the last decade, it remains a challenging task because the appearances of people can seem extremely different across diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person re-identification by analyzing camera viewpoints and person poses called pose-aware multi-shot matching...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29698205/image-segmentation-for-intensity-inhomogeneity-in-presence-of-high-noise
#11
Haider Ali, Lavdie Rada, Noor Badshah
Automated segmentation of fine objects details in a given image is becoming of crucial interest in different imaging fields. In this paper, we propose a new variational level-set model for both global and interactive\selective segmentation tasks, which can deal with intensity inhomogeneity and the presence of noise. The proposed method maintains the same performance on clean and noisy vector-valued images. The model utilizes a combination of locally computed denoising constrained surface and a denoising fidelity term to ensure a fine segmentation of local and global features of a given image...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29698204/subspace-clustering-via-learning-an-adaptive-low-rank-graph
#12
Ming Yin, Shengli Xie, Zongze Wu, Yun Zhang, Junbin Gao
By using a sparse representation or low-rank representation of data, the graph-based subspace clustering has recently attracted considerable attention in computer vision, given its capability and efficiency in clustering data. However, the graph weights built using the representation coefficients are not the exact ones as the traditional definition is in a deterministic way. The two steps of representation and clustering are conducted in an independent manner, thus an overall optimal result cannot be guaranteed...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29698203/too-far-to-see-not-really-pedestrian-detection-with-scale-aware-localization-policy
#13
Xiaowei Zhang, Li Cheng, Bo Li, Hai-Miao Hu
A major bottleneck of pedestrian detection lies on the sharp performance deterioration in the presence of small-size pedestrians that are relatively far from the camera. Motivated by the observation that pedestrians of disparate spatial scales exhibit distinct visual appearances, we propose in this paper an active pedestrian detector that explicitly operates over multiple-layer neuronal representations of the input still image. More specifically, convolutional neural nets, such as ResNet and faster R-CNNs, are exploited to provide a rich and discriminative hierarchy of feature representations, as well as initial pedestrian proposals...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29698202/progressive-hard-mining-network-for-monocular-depth-estimation
#14
Zhenyu Zhang, Chunyan Xu, Jian Yang, Junbin Gao, Zhen Cui
Depth estimation from the monocular RGB image is a challenging task for computer vision due to no reliable cues as the prior knowledge. Most existing monocular depth estimation works including various geometric or network learning methods lack of an effective mechanism to preserve the cross-border details of depth maps, which yet is very important for the performance promotion. In this paper, we propose a novel end-to-end progressive hard-mining network (PHN) framework to address this problem. Specifically, we construct the hard-mining objective function, the intra-scale and inter-scale refinement subnetworks to accurately localize and refine those hard-mining regions...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29671748/retina-inspired-filter
#15
Effrosyni Doutsi, Lionel Fillatre, Marc Antonini, Julien Gaulmin
This paper introduces a novel filter, which is inspired by the human retina. The human retina consists of three different layers: the Outer Plexiform Layer (OPL), the inner plexiform layer, and the ganglionic layer. Our inspiration is the linear transform which takes place in the OPL and has been mathematically described by the neuroscientific model "virtual retina." This model is the cornerstone to derive the non-separable spatio-temporal OPL retina-inspired filter, briefly renamed retina-inspired filter, studied in this paper...
July 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29671747/learning-view-specific-deep-networks-for-person-re-identification
#16
Zhanxiang Feng, Jianhuang Lai, Xiaohua Xie
In recent years, a growing body of research has focused on the problem of person re-identification (re-id). The re-id techniques attempt to match the images of pedestrians from disjoint non-overlapping camera views. A major challenge of the re-id is the serious intra-class variations caused by changing viewpoints. To overcome this challenge, we propose a deep neural network-based framework which utilizes the view information in the feature extraction stage. The proposed framework learns a view-specific network for each camera view with a cross-view Euclidean constraint (CV-EC) and a cross-view center loss...
July 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29671746/spatio-temporal-attention-based-lstm-networks-for-3d-action-recognition-and-detection
#17
Sijie Song, Cuiling Lan, Junliang Xing, Wenjun Zeng, Jiaying Liu
Human action analytics has attracted a lot of attention for decades in computer vision. It is important to extract discriminative spatio-temporal features to model the spatial and temporal evolutions of different actions. In this paper, we propose a spatial and temporal attention model to explore the spatial and temporal discriminative features for human action recognition and detection from skeleton data. We build our networks based on the recurrent neural networks with long short-term memory units. The learned model is capable of selectively focusing on discriminative joints of skeletons within each input frame and paying different levels of attention to the outputs of different frames...
July 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29671745/towards-optimal-denoising-of-image-contrast
#18
Wu Cheng, Keigo Hirakawa
Most conventional imaging modalities detect light indirectly by observing high-energy photons. The random nature of photon emission and detection is often the dominant sources of noise in imaging. Such case is referred to as photon-limited imaging, and the noise distribution is well modeled as Poisson. Multiplicative multiscale innovation (MMI) presents a natural model for Poisson count measurement, where the inter-scale relation is represented as random partitioning (binomial distribution) or local image contrast...
July 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29671744/full-scale-regression-based-injection-coefficients-for-panchromatic-sharpening
#19
Gemine Vivone, Rocco Restaino, Jocelyn Chanussot
Pansharpening is usually related to the fusion of a high spatial resolution but low spectral resolution (panchromatic) image with a high spectral resolution but low spatial resolution (multispectral) image. The calculation of injection coefficients through regression is a very popular and powerful approach. These coefficients are usually estimated at reduced resolution. In this paper, the estimation of the injection coefficients at full resolution for regression-based pansharpening approaches is proposed. To this aim, an iterative algorithm is proposed and studied...
July 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29671743/an-embarrassingly-simple-approach-to-visual-domain-adaptation
#20
Hao Lu, Chunhua Shen, Zhiguo Cao, Yang Xiao, Anton van den Hengel
We show that it is possible to achieve high-quality domain adaptation without explicit adaptation. The nature of the classification problem means that when samples from the same class in different domains are sufficiently close, and samples from differing classes are separated by large enough margins, there is a high probability that each will be classified correctly. Inspired by this, we propose an embarrassingly simple yet effective approach to domain adaptation-only the class mean is used to learn class-specific linear projections...
July 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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