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Investigation on the blurred image restoration based on brain-inspired model

IET image processing 2023年第1期17卷 12-23页

作者： Xiangyan, Meng Chen, Zhao Li, Zhao Keding, Yan Yumiao, Ren Haixian, Pan Xian Technol Univ Elect Engn Inst Xian Shaanxi Peoples R China

Though relatively good effect has been achieved by the image de-blurring method based on deep learning, the existing methods still suffer from the problem of unclear restoration of the edges. Therefore, brain-inspired image restoration model based on human attention and "fine vision" is proposed to improve the blind restoration quality of the image in this paper according to the response mechanism of the different cerebral cortices for high and low spatial resolutions. The designed brain-inspired model consists of dual-channel network available to realize the function of feature merger for low and high resolutions, which is used to extract the image edges with detailed information filtered out. Confirmatory experiment is implemented based on the blurred image in the data set of GOPRO, LIVE and set14. As per the result, the model proposed is available for relatively good restoration of blurred image and super-resolution, as well as looking results by visual inspection.

关键词： blind restoration quality image edges low spatial resolutions relatively good restoration deep learning de-blurring method brain-inspired image restoration model Neural nets Optical, image and video signal processing computer vision and image processing techniques high resolutions image restoration low resolutions different cerebral cortices human attention deep learning (artificial intelligence) blurred image restoration designed brain-inspired model relatively good effect unclear restoration image reconstruction brain image resolution

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Smart pansharpening approach using kernel-based image filtering

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IET image processing 2021年第11期15卷 2629-2642页

作者： Al Smadi, Ahmad Yang, Shuyuan Mehmood, Atif Abugabah, Ahed Wang, Min Bashir, Muzaffar Xidian Univ Sch Artificial Intelligence 2 South Taibai Rd Xian 710071 Peoples R China Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Xidian Univ Key Lab Radar Signal Proc Xian Peoples R China Univ Punjab Dept Phys Lahore Pakistan

Remote sensing image fusion plays important roles in numerous applications, including monitoring, metrology, and agriculture. image fusion gathers essential information from several image sources and consolidates them into a single image called a fused image. The fused image involves relevant data, and it is more informative than any other images extracted from one source. This study proposed a pansharpening technique based on image filtering utilising a bilateral filter to generate high-frequency details from panchromatic image. The various types of side window guided filters are employed to enhance the multispectral band from panchromatic image and then used these filters to adjust spatial data misfortune that happens when images are combined. Experimental results demonstrated that the proposed method provides consistent results concise with reported by the previous research in terms of subjective and objective assessments on remote sensing data.

关键词： image resolution remote sensing data pansharpening technique Filtering methods in signal processing image colour analysis image filtering panchromatic image sensing image fusion computer vision and image processing techniques image fusion remote sensing geophysical image processing image fusion gathers essential information Optical, image and video signal processing single image fused image filtering theory Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research smart pansharpening approach image sources bilateral filter side window guided filters

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Automatic two-stage IR and MMW image registration algorithm for concealed weapons detection

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IEE PROCEEDINGS-vision image AND SIGNAL processing 2001年第4期148卷 209-216页

作者： Chen, HM Varshney, PK Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

A two-stage registration scheme for the concealed weapons detection (CWD) problem is developed. The goal is to automatically register images taken simultaneously from two different (infrared (IR) and millimetre wave (MMW)) but parallel sensors whose lines of sight (LOS) are close to each other. The purpose of the first stage is to register the images coarsely. A feature-based image registration algorithm based on human body silhouettes is developed at this stage. The pose parameters found at this stage are used as the starting search point for the second stage of the registration algorithm. At the second stage, maximisation of the mutual information measure between IR and MMW images is performed to improve the pose parameters obtained at the first stage. Two-dimensional partial volume interpolation is employed to estimate the joint histogram that is needed to calculate mutual information. The simplex search algorithm is utilised to maximise the MI measure. In both stages, the distortion between the two images is assumed to be a rigid body transformation. Experimental results indicate that the automated two-stage registration algorithm performs fairly well.

关键词： automatic two-stage MMW image registration pose parameters feature-based image registration algorithm rigid body transformation millimetre wave imaging human body silhouettes feature extraction image distortion mutual information measure maximisation computer vision and image processing techniques search problems Other military topics Interpolation and function approximation (numerical analysis) 2D partial volume interpolation joint histogram estimation Optical, image and video signal processing weapons millimetre wave sensor infrared sensor image registration mutual information measure interpolation infrared imaging concealed weapons detection simplex search algorithm image sensors automatic two-stage IR image registration

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Leveraging attention-based visual clue extraction for image classification

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IET image processing 2021年第12期15卷 2937-2947页

作者： Cui, Yunbo Du, Youtian Wang, Xue Wang, Hang Su, Chang Xi An Jiao Tong Univ Fac Elect & Informat Engn 28 Xianning West Rd Xian Peoples R China Leiden Univ Inst Adv Comp Sci Leiden Netherlands Cornell Univ Weill Cornell Med Dept Healthcare Policy & Res Ithaca NY USA

Deep learning-based approaches have made considerable progress in image classification tasks, but most of the approaches lack interpretability, especially in revealing the decisive information causing the categorization of images. This paper seeks to answer the question of what clues encode the discriminative visual information between image categories and can help improve the classification performance. To this end, an attention-based clue extraction network (ACENet) is introduced to mine the decisive local visual information for image classification. ACENet constructs a clue-attention mechanism, that is global-local attention, between the image and visual clue proposals extracted from it and then introduces a contrastive loss defined over the achieved discrete attention distribution to increase the discriminability of clue proposals. The loss encourages considerable attention to be devoted to discriminative clue proposals, that is those similar within the same category and dissimilar across categories. The experimental results for the Negative Web image (NWI) dataset and the public imageNet2012 dataset demonstrate that ACENet can extract true clues to improve the image classification performance and outperforms the baselines and the state-of-the-art methods.

关键词： image recognition computer vision and image processing techniques Data mining Neural nets

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Facial expression recognition based on geometric and optical flow features in colour image sequences

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IET computer vision 2012年第2期6卷 79-89页

作者： Niese, R. Al-Hamadi, A. Farag, A. Neumann, H. Michaelis, B. Univ Magdeburg Inst Elect Signal Proc & Commun D-39106 Magdeburg Germany Univ Louisville Comp Vis & Image Proc Lab Louisville KY 40292 USA Univ Ulm Inst Neural Informat Proc Ulm Germany

Facial expression recognition is a useful feature in modern human computer interaction (HCI). In order to build efficient and reliable recognition systems, face detection, feature extraction and classification have to be robustly realised. Addressing the latter two issues, this work proposes a new method based on geometric and transient optical flow features and illustrates their comparison and integration for facial expression recognition. In the authors' method, photogrammetric techniques are used to extract three-dimensional (3-D) features from every image frame, which is regarded as a geometric feature vector. Additionally, optical flow-based motion detection is carried out between consecutive images, what leads to the transient features. Artificial neural network and support vector machine classification results demonstrate the high performance of the proposed method. In particular, through the use of 3-D normalisation and colour information, the proposed method achieves an advanced feature representation for the accurate and robust classification of facial expressions.

关键词： User interfaces geometric feature vector image recognition neural nets support vector machine classification photogrammetry facial expression recognition HCI support vector machines advanced feature representation feature classification image sequences colour information image frame photogrammetric technique facial expression classification face recognition optical flow-based motion detection face detection 3D normalisation image classification transient optical flow feature feature extraction three-dimensional feature extraction image colour analysis image motion analysis computer vision and image processing techniques human computer interaction artificial neural network colour image sequence image representation Neural computing techniques Knowledge engineering techniques 3D feature extraction

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Denseformer: A dense transformer framework for person re-identification

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IET computer vision 2023年第5期17卷 527-536页

作者： Ma, Haoyan Li, Xiang Yuan, Xia Zhao, Chunxia Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Transformer has shown its effectiveness and advantage in many computer vision tasks, for example, image classification and object re-identification (ReID). However, existing vision transformers are stacked layer by layer, lacking direct information exchange among every layer. Inspired by DenseNet, we propose a dense transformer framework (termed Denseformer) that connects each layer to every other layer through class tokens. We demonstrate that Denseformer can consistently achieve better performance on person ReID tasks across datasets (Market-1501, DukeMTMC, MSMT17, and Occluded-Duke), only at a negligible increase of computation. We show that Denseformer has several compelling advantages: it pays more attention to the main parts of human bodies and obtains discriminative global features.

关键词： existing vision transformers feature extraction direct information exchange computer vision deep learning (artificial intelligence) Neural nets image recognition advantage image classification person ReID tasks Optical, image and video signal processing person re-identification dense transformer framework computer vision and image processing techniques computer vision tasks Denseformer

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Towards accurate classification of skin cancer from dermatology images

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IET image processing 2021年第9期15卷 1971-1986页

作者： Gautam, Anjali Raman, Balasubramanian Indian Inst Informat Technol Dept Informat Technol Prayagraj Uttar Pradesh India Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee Uttarakhand India

Skin cancer is the most well-known disease found in the individuals who are exposed to the Sun's ultra-violet (UV) radiations. It is identified when skin tissues on the epidermis grow in an uncontrolled manner and appears to be of different colour than the normal skin tissues. This paper focuses on predicting the class of dermascopic images as benign and malignant. A new feature extraction method has been proposed to carry out this work which can extract relevant features from image texture. Local and gradient information from x and y directions of images has been utilized for feature extraction. After that images are classified using machine learning algorithms by using those extracted features. The efficacy of the proposed feature extraction method has been proved by conducting several experiments on the publicly available image dataset 2016 International Skin Imaging Collaboration (ISIC 2016). The classification results obtained by the method are also compared with state-of-the-art feature extraction methods which show that it performs better than others. The evaluation criteria used to obtain the results are accuracy, true positive rate (TPR) and false positive rate (FPR) where TPR and FPR are used for generating receiver operating characteristic curves.

关键词： Optical, image and video signal processing image recognition Biomedical measurement and imaging computer vision and image processing techniques Optical and laser radiation (medical uses) Patient diagnostic methods and instrumentation Biology and medical computing

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Residual dense collaborative network for salient object detection

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IET image processing 2023年第2期17卷 492-504页

作者： Han, Yibo Wang, Liejun Cheng, Shuli Li, Yongming Du, Anyu Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China

Owing to the renaissance of deep convolutional neural networks (CNN), salient object detection based on fully convolutional neural networks (FCNs) has attracted widespread attention. However, the scale variation of prominent objects, complex background features and fuzzy edges have historically been a great challenge to us. All these are closely associated with the utilization of multi-level and multi-scale features. At the same time, deep learning methods meet the challenges of computation and memory consumption in practice. To address these problems, the authors propose a different salient object detection method based on residuals learning and dense fusion learning framework. The proposed network is named Residual Dense Collaborative Network (RDCNet). First of all, the authors design a multi-layer residual learning (MRL) module to extract salient object features in more detail, getting the utmost out of the object's multi-scale and multi-level information. Then, on the basis of the vigoroso stage-wise convolution feature, the authors put forward the dilated convolution module (DCM) to acquire a rough global saliency map. Finally, the final accurate saliency detection map is obtained through dense cooperation learning (DCL), and the remaining learning is also used to improve gradually, so as to achieve high compactness and high-efficiency results. Experimental results show that this method is the most advanced method for five widely used datasets (DUTS-TE, HKU-IS, PASCAL-S, ECSSD, DUT-OMRON) without any pre-processing and post-processing. Especially on the ECSSD dataset, the F-measure of RDCNet achieves 95.2%.

关键词： feature extraction residual dense collaborative network multiscale features Residual dense collaborative network remaining learning prominent objects Neural nets multilevel information final accurate saliency detection map computer vision and image processing techniques dense cooperation learning convolutional neural nets salient object features fully convolutional neural networks object detection salient object detection method deep learning (artificial intelligence) image recognition dense fusion learning framework deep learning methods fuzzy set theory scale variation dilated convolution module vigoroso stage-wise convolution feature complex background features Combinatorial mathematics multilayer residual learning module

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Multi-view convolutional neural network-based target classification in high-resolution automotive radar sensor

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IET RADAR SONAR AND NAVIGATION 2023年第1期17卷 15-26页

作者： Kwak, Seungheon Kim, Hangyeol Kim, Gun Lee, Seongwook Korea Aerosp Univ Sch Elect & Informat Engn Coll Engn 76Hanggongdaehak ro Goyangsi 10540 Gyeonggido South Korea

In this study, a target classification method based on point cloud data in a high-resolution radar sensor is proposed. By using multiple antenna elements arranged in horizontal and vertical directions, pedestrians, cyclists and vehicles can be expressed as point cloud data in the three-dimensional (3D) space. To perform target classification using the spatial characteristics (i.e. length, height and width) of the target, the 3D point cloud data is orthogonally projected onto the xy, yz and zx planes, respectively, and three types of images are generated. Then, a multi-view convolutional neural network (CNN)-based target classifier using those three images as inputs is designed. To this end, a method for synthesising the detection results of three directions in series or in parallel is proposed. The proposed classifier can learn the spatial characteristics of the target by using the detection results of multiple viewpoints. Compared to the CNN-based classifier that uses only the detection result of a single plane as input, the proposed method shows 4.5%p higher classification accuracy in terms of the target with the lowest classification accuracy. In addition, the proposed multi-view CNN structure shows improved classification performance and shorter training time compared to the well-known deep learning methods for image classification.

关键词： lowest classification accuracy p higher classification accuracy multiview CNN structure feature extraction image classification three-dimensional space target classification method learning (artificial intelligence) Radar equipment, systems and applications pattern classification Neural nets Optical, image and video signal processing computer vision and image processing techniques high-resolution radar sensor convolutional neural nets CNN-based classifier deep learning (artificial intelligence) image recognition high-resolution automotive radar sensor multiview convolutional neural network-based target classification point cloud data spatial characteristics road vehicle radar vertical directions multiview convolutional neural network-based target classifier horizontal directions classification performance multiple antenna elements

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Spectral sensitivity design for maximum colour separation in artificial colour systems

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IET image processing 2009年第3期3卷 135-146页

作者： Heidary, K. Caulfield, H. J. Alabama A&M Univ EE Dept Normal AL 35762 USA Alabama A&M Univ Res Inst Normal AL 35762 USA

Engineers have utilised spectral information and have steadily improved its applications in imaging systems for more than a century. The course of technological developments in colour imaging has been dictated by system improvements measured by their efficacy for direct human consumption. It seems reasonable to us to try to emulate nature and boost capabilities of machine vision systems by optimising the way in which they exploit spectral information. This is a two-step process: first step involves using a few spectrally broad detectors to compress the information content of the scene and the second step constructs spectral discriminants for image segmentation based on a small number of spectrally generated features assigned to each pixel. In animals the discriminant value is attributed to the object as what is called colour. Previous papers have concentrated on the final segmentation step. Here we show a straightforward way to design application-specific spectral sensitivity functions to improve image segmentation. The resulting functions can be used for reliable recognition of objects in a hyperspectral image in real time. These functions can also be used to design task-specific specialised cameras that can outperform current hyperspectral systems in terms of sensitivity, size, power consumption, robustness, price and complexity.

关键词： application-specific spectral sensitivity function artificial colour system computer vision and image processing techniques machine vision system maximum colour separation computer vision Optical, image and video signal processing image colour analysis image segmentation

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