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Wavelet-based image denoising with the normal inverse Gaussian prior and linear MMSE estimator

IET image processing 2008年第4期2卷 203-217页

作者： Bhuiyan, M. I. H. Ahmad, M. O. Swamy, M. N. S. Concordia Univ Dept Elect & Comp Engn Ctr Commun & Signal Proc Montreal PQ H3G 1M8 Canada

A new spatially adaptive wavelet-based method is introduced for reducing noise in images corrupted by additive white Gaussian noise. It is shown that a symmetric normal inverse Gaussian distribution is highly suitable for modelling the wavelet coefficients. In order to estimate the parameters of the distribution, a maximum-likelihood-based technique is proposed, wherein the Gauss-Hermite quadrature approximation is exploited to perform the maximisation in a computationally efficient way. A Bayesian minimum mean-squared error (MMSE) estimator is developed utilising the proposed distribution. The variances corresponding to the noise-free coefficients are obtained from the Bayesian estimates using a local neighbourhood. A modified linear MMSE estimator that incorporates both intra-scale and inter-scale dependencies is proposed. The performance of the proposed method is studied using typical noise-free images corrupted with simulated noise and compared with that of the other state-of-the-art methods. It is shown that the proposed method gives higher values of the peak signal-to-noise ratio compared with most of the other denoising techniques and provides images of good visual quality. Also, the performance of the proposed method is quite close to that of the state-of-the-art Gaussian scale mixture (GSM) method, but with much less complexity.

关键词： Bayesian minimum mean-squared error estimator Bayesian estimates adaptive estimation maximisation symmetric normal inverse Gaussian distribution adaptive wavelet-based method noise-free coefficients normal inverse Gaussian prior Gaussian scale mixture computer vision and image processing techniques Other topics in statistics linear MMSE estimator noise-free images Optical, image and video signal processing Mathematical analysis Integral transforms visual quality Gauss-Hermite quadrature approximation maximum likelihood estimation signal-to-noise ratio Bayes methods Gaussian distribution wavelet coefficients wavelet transforms Interpolation and function approximation (numerical analysis) parameter estimation inverse problems maximum-likelihood-based technique image denoising computational complexity additive white Gaussian noise least mean squares methods wavelet-based image denoising

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On learning based compressed sensing for high resolution image reconstruction

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IET image processing 2021年第2期15卷 393-404页

作者： Islam, Sheikh Rafiul Maity, Santi P. Ray, Ajoy Kumar Indian Inst Engn Sci & Technol Dept Informat Technol Howrah 711103 W Bengal India Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur W Bengal India

Compressed sensing (CS) or compressive sampling has shown an enormous potential to reconstruct a signal from its highly under-sampled observations. A high dimensional image processing system can adopt the CS paradigm to reduce the storage and the transmission burden. However, a large sensing system (matrix) is required to capture high dimensional images. The CS reconstruction algorithms demand heavy computational requirement due to the use of large sensing matrix. It sometimes becomes impractical to implement a sensing system of the desired size due to the limited access to the CPU memory. To address this issue, the present work proposes a deep learning based CS framework that uses a convolutional neural network to enable capturing of a high dimensional image by utilizing a multi-layer subsampling and filtering operations. The proposed approach uses another convolutional neural network that reconstructs the original image without depending on the sensing network at a significantly reduced computational cost. Extensive simulation results show that the proposed method reconstructs a high dimensional image with an improved PSNR value by 1.5 +/- 0.63 dB, SSIM value by 0.04 +/- 0.02 and FSIM value by 0.02 +/- 0.02 compared to the other state-of-the-art methods in less than 1.4 seconds.

关键词： image and video coding computer vision and image processing techniques Neural nets

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Learn from Object Counting: Crowd Counting with Meta-learning

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IET image processing 2021年第14期15卷 3543-3550页

作者： Zan, Changtong Liu, Baodi Guan, Weili Zhang, Kai Liu, Weifeng China Univ Petr East China Qingdao Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Monash Univ Fac Informat Technol Clayton Campus Clayton Vic Australia

The objective of crowd counting is to learn a counter that can estimate the number of people in a single image. So far, most of the proposed work evaluates the crowd density by fitting the constructed density map corresponding to the sample. The performance of those algorithms depends on a large amount of carefully prepared data. However, a significant problem with crowd data sets is the difficulty of labeling. To address such a situation, utilizing object counting data in few-shot scenes is considered and an efficient algorithm to extract the meta-information is proposed, thus improving the accuracy and convergence rate of the crowd counting tasks. Specifically, the counting network is trained with only object counting tasks constructed on different domains during the meta-training phase. Then, the meta-counter is testing on crowd counting tasks in the meta-testing stage. Experimentally, it is demonstrated that the above way improves the converge rate and accuracy of crowd counting tasks on three crowd counting datasets when meta-training on ten-type object counting tasks.

关键词： Optical, image and video signal processing computer vision and image processing techniques Video signal processing Machine learning (artificial intelligence)

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Global weighted average pooling network with multilevel feature fusion for weakly supervised brain tumor segmentation

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IET image processing 2023年第2期17卷 418-427页

作者： Li, Zi-Wei Xuan, Shi-Bin He, Xue-Dong Wang, Li Guangxi Minzu Univ Sch Artificial Intelligence Daxue East Rd 188 Nanning Peoples R China Guangxi Key Lab Hybtid Computat & IC Design Anal Nanning Peoples R China

Medical image segmentation plays a vital role in computer-aided diagnosis and intelligent medical treatment. It can preprocess medical images to help doctors better diagnose diseases. Class activation map (CAM) is an important technology in weakly supervised segmentation, which can achieve image segmentation without pixel-level label training. This technology can well meet the needs of medical image segmentation. However, CAM obtaining is still unperfect due to global average pooling (GAP). GAP will cause important and non-important regions to be given equal attention during the training process. So, CAM cannot demarcate the boundary of the target regions well. In order to solve this problem, a global weighted average pooling network fusing the grayscale information of medical images is proposed. The proposed network can solve the problem that GAP has the same concern for important regions and non-important regions of the feature map, because the different weights can be learned for different positions of the feature map before the GAP in the proposed model. At the same time, because of the grayscale difference between the tumor area and the non-tumor area in the brain tumor image, the low-level grayscale information of the medical image is fused with the high-level semantic information extracted by the network to learn the weights. This operation gives full play to the advantages of feature maps of different levels. The experiment results on the popular medical image dataset BraTS2019 show that the proposed method can well improve the performance of CAM and help CAM fit the boundaries of objects. Meanwhile, in the DSC evaluation, the proposed method achieves a score of 64.1%, which is a 4.6% improvement over a recent research method.

关键词： global average pooling Biology and medical computing supervised learning image segmentation feature extraction medical image processing diseases multilevel feature fusion pixel-level label training target regions global weighted average pooling network CAM obtaining brain tumor image high-level semantic information weakly supervised brain tumor segmentation Neural nets medical image segmentation Optical, image and video signal processing image fusion computer vision and image processing techniques intelligent medical treatment feature map popular medical image dataset BraTS weakly supervised segmentation nontumor area image recognition computer-aided diagnosis brain low-level grayscale information class activation map nonimportant regions Patient diagnostic methods and instrumentation important regions tumours Biomedical measurement and imaging

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Foreground segmentation for dynamic scenes with sudden illumination changes

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IET image processing 2012年第5期6卷 606-615页

作者： Li, J. Miao, Z. Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China

Foreground segmentation is a very difficult task in dynamic background, which is common in real-world environments, caused by for example waving foliage, rippling water or illumination changes owing to light switching etc. A large number of methods for foreground segmentation in dynamic background have been proposed in the past decades, but most of them can only handle repetitive movements or gradual changes in background, and fail when sudden illumination changes occur. This study proposes a new method, which can deal with both repetitive movements and gradual/sudden illumination changes. The authors use a two-layer Gaussian mixture model to represent the background under different lighting conditions and formulate a joint posterior function of background state and segmentation based on the learned model. Given a new image, the background state and foreground segmentation are simultaneously optimised in a Bayesian perspective using a nested two-layer optimisation. The authors test their method on several image sequences and compare the results qualitatively and quantitatively with some state-of-the-art methods to demonstrate the effectiveness of the method.

关键词： two-layer Gaussian mixture model gradual-sudden illumination change computer vision and image processing techniques lighting waving foliage background state light switching dynamic scene foreground segmentation Bayesian perspective Gaussian processes Optical, image and video signal processing image segmentation rippling water nested two-layer optimisation joint posterior function image sequences Bayes methods Other topics in statistics image sequence

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Spectral recovery-guided hyperspectral super-resolution using transfer learning SRTL for HSISR

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IET image processing 2021年第11期15卷 2656-2665页

作者： Zhang, Shaolei Fu, Guangyuan Wang, Hongqiao Zhao, Yuqing Xian High Tech Res Inst Dept Informat Engn Xian 710025 Peoples R China

Single hyperspectral image (HSI) super-resolution (SR) has attracted researcher's attention;however, most existing methods directly model the mapping between low- and high-resolution images from an external training dataset, which requires large memory and computing resources. Moreover, there are few such available training datasets in real cases, which prevent deep-learning-based methods from further improving performance. Here, a novel single HSI SR method based on transfer learning is proposed. The proposed method is composed of two stages: spectral down-sampled image SR reconstruction based on transfer learning and HSI reconstruction via spectral recovery module. Instead of directly applying the learned knowledge from the colour image domain to HSI SR, the spectral down-sampled image is fed into a spatial SR model to obtain a high-resolution image, which acts as a bridge between the colour image and HSI. The spectral recovery network is used to restore the HSI from the bridge image. In addition, pre-training and collaborative fine-tuning are proposed to promote the performance of SR and spectral recovery. Experiments on two public HSI datasets show that the proposed method achieves promising SR performance with a small paired HSI dataset.

关键词： Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Optical, image and video signal processing computer vision and image processing techniques

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Pain expression assessment based on a locality and identity aware network

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IET image processing 2021年第12期15卷 2948-2958页

作者： Xin, Xuwu Li, Xiaowu Yang, Shengfu Lin, Xiaoyan Zheng, Xin Hosp Shantou Univ Affiliated Hosp 2 Med Coll Shantou 515000 Peoples R China Jinan Univ Affiliated Hosp 1 Guangzhou Peoples R China Joint Shantou Int Eye Ctr Shantou Univ & Chinese Shantou Peoples R China

In clinical medicine, the pain feeling is a significant indicator for the medical condition of patients. Of late, automatic pain assessment methods have received more and more interests. Many researchers proposed corresponding methods and achieved impressive results. However, they always ignore the locality and individual differences of painful expression. Therefore, a locality and identity aware network (LIAN) for pain assessment is presented here. Concretely, for the locality characteristic, a locality aware module consisting of a two-branch structure, feature and attention branches, is presented. The former learns pain features by a deep network, while the latter guides the pain features to focus on the discriminational regions of pain. As for the individual differences, an identity aware module with a multi-task method is proposed to represent identity-related information to achieve identity-invariant pain assessment. Extensive experiments on public databases show the superiority of LIAN in pain assessment.

关键词： image recognition computer vision and image processing techniques Biology and medical computing

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Multi-component image segmentation using a hybrid dynamic genetic algorithm and fuzzy C-means

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IET image processing 2009年第2期3卷 52-62页

作者： Awad, M. Chehdi, K. Nasri, A. Natl Council Sci Res Ctr Remote Sensing Beirut Lebanon Univ Rennes 1 ENSSAT TSI2M IETR Lannion Lannion France Amer Univ Beirut Dept Comp Sci Beirut Lebanon

image segmentation is an important task in image analysis and processing. Many of the existing methods for segmenting a multi-component image (satellite or aerial) are very slow and require a priori knowledge of the image that could be difficult to obtain. Furthermore, the success of each of these methods depends on several factors, such as the characteristics of the acquired image, resolution limitations, intensity in-homogeneities and the percentage of imperfections induced by the process of image acquisition. Recently, fuzzy C-means (FCM) and Genetic Algorithms were separately used in segmenting multi-component images but neither of them had successfully addressed the above concerns. GA was enhanced using Hill-climbing, randomising, and modified mutation operators, leading to what is called hybrid dynamic genetic algorithm (HDGA). Coupling HDGA and FCM creates an unsupervised segmentation method which could successfully segment two types of multi-component images (Landsat ETM+, and IKONOS II). Comparison with the four different methods FCM, hybrid genetic algorithm (HGA), self-organizing-maps (SOM), and the combination of SOM and HGA (SOM-HGA) reveals that FCM-HDGA segmentation method gives robust and reliable results, and is more time efficient.

关键词： image resolution modified mutation operators hybrid dynamic genetic algorithm computer vision and image processing techniques Hill-climbing Optical, image and video signal processing fuzzy C-means genetic algorithms Combinatorial mathematics multicomponent image segmentation randomising self-organizing-maps fuzzy set theory Optimisation techniques image acquisition image segmentation

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A two-step abnormal data analysis and processing method for millimetre-wave radar in traffic flow detection applications

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IET INTELLIGENT TRANSPORT SYSTEMS 2021年第5期15卷 671-682页

作者： Liu, Haiqing Teng, Kunmin Rai, Laxmisha Zhang, Yu Wang, Shengli Shandong Univ Sci & Technol Coll Transportat Qingdao Peoples R China Shandong Univ Sci & Technol Coll Elect & Informat Engn Qingdao Shandong Peoples R China Shandong Univ Sci & Technol Ocean Sci & Engn Coll Qingdao Peoples R China

In a new application scenario where the millimetre-wave radar is installed above the road for detecting traffic flow in downward looking direction, the original data of the radar includes all kinds of background noises and false targets. In order to acquire effective vehicle trajectories, a two-step abnormal data processing method for millimetre-wave radar in traffic flow detection application is proposed. In the first step, the rational range of distance, angle and speed are studied, and proper thresholds are presented for reducing the samples of which the single parameter is with the obvious abnormality. Moreover, the nearest neighbour analysis method is used to further extract vehicle trajectories based on the similarity and slope characteristics of each sample to its neighbours. Taking actual detected data as samples, the weighting coefficients, similarity threshold, average slope threshold and standard deviation threshold are calibrated for the proposed nearest neighbour analysis method. The two-step processing method presents a higher performance in extracting effective trajectory samples, and the ratio of noise points is reduced to 4.1%, compared with 239.9% in the original data sample. The proposed method can provide an effective reference for further applications, such as driving behaviour analysis and traffic flow parameter identification.

关键词： Optical, image and video signal processing Radar equipment, systems and applications computer vision and image processing techniques Data handling techniques Traffic engineering computing Other learning models (inc. Naive Bayes)

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Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval

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IET image processing 2021年第5期15卷 1093-1104页

作者： Paramarthalingam, Arjun Thankanadar, Mirnalinee Univ Coll Engn Comp Sci & Engn Villupuram 605103 Tamil Nadu India SSN Coll Engn Comp Sci & Engn Kalavakkam Tamil Nadu India

Shape recognition and retrieval is a complex task on non-rigid objects and it can be effectively performed by using a set of compact shape descriptors. This paper presents a new technique for generating normalised contour points from shape silhouettes, which involves the identification of object contour from images and subsequently the object area normalisation (OAN) method is used to partition the object into equal part area segments with respect to shape centroid. Later, these contour points are used to derive six descriptors such as compact centroid distance (CCD), central angle (ANG), normalized points distance (NPD), centroid distance ratio (CDR), angular pattern descriptor (APD) and multi-triangle area representation (MTAR). These descriptors are a 1D shape feature vector which preserve contour and region information of the shapes. The performance of the proposed descriptors is evaluated on MPEG-7 Part-A, Part-B and multi-view curve dataset images. The present experiments are aimed to check proposed shape descriptor's robustness to affine invariance property and image retrieval performance. Comparative study has also been carried out for evaluating our approach with other state of the art approaches. The results show that image retrieval rate in OAN approach performs significantly better than that in other existing shape descriptors.

关键词： Algebra image recognition computer vision and image processing techniques Combinatorial mathematics Algebra Combinatorial mathematics Information retrieval techniques

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