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A probabilistic collaborative dictionary learning-based approach for face recognition

IET image processing 2021年第4期15卷 868-884页

作者： Lv, Shilin Liang, Jiuzhen Di, Lan Yunfei, Xia Hou, ZhenJie Changzhou Univ Sch Comp Sci & Artificial Intelligence Changzhou 213164 Jiangsu Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Univ North Carolina Charlotte Dept Math & Stat Charlotte NC USA

Although Sparse Representation based Classifier (SRC), a non-parametric model, can obtain an interesting result for pattern recognition , a reasonable interpretation has been lacked for its classification mechanism. What is more, the training samples are used as off-the-shelf dictionary directly in SRC, which can make the feature hidden in the training samples hard be extracted. At the same time, the complexity of the algorithm is increased because of too many atoms of the dictionary. The authors first explains in detail the classification mechanism of SRC from the view of probabilistic collaborative subspace and offer the process to improve the stability of the algorithm using the joint probability in the case of the multi-subspace. Then, the authors introduce the dictionary learning (DL) and Fisher criterion into the model to further enhance the discrimination of the coding coefficient. In order to ensure the convexity of the discrimination term and further enhance the discrimination, the authors add the L-21-norm term into the Fisher discrimination term and offer the proof for its convexity. Finally, the experimental result on a series of benchmark databases, such as AR, Extended Yale B, LFW3D-hassner, LFW3D-sdm and LFW3D-Dlib, show that PCDDL outperforms existing classical classification models.

关键词： image recognition computer vision and image processing techniques Data handling techniques Other topics in statistics Other topics in statistics

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Medical image fusion and noise suppression with fractional-order total variation and multi-scale decomposition

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IET image processing 2021年第8期15卷 1688-1701页

作者： Zhang, Xuefeng Yan, Hui Northeastern Univ Coll Sci Shenyang 110819 Peoples R China

Fusion and noise suppression of medical images are becoming increasingly difficult to be ignored in image processing, and this technique provides abundant information for the clinical diagnosis and treatment. This paper proposes a medical image fusion and noise suppression model in pixel level. This model decomposes the original image into a noiseless base layer, a large-scale noiseless detail layer and a small-scale detail layer which contains details and noise information. The fractional-order derivative and saliency detection are used to construct the weight functions to fuse the base layers. The proposed total variation model combines the fractional-order derivative to fuse the small-scale detail layers. The mathematical properties and time complexity of the total variation model are also analysed. And choose-max method is used to fuse the large-scale detail medical layers simply. Our approach is based on fractional-order derivative, which enables keep more information and decrease blocky effects more effectively compared with the integer-order derivative. To verify the validity, the proposed method is compared with some fusion methods in the subjective and objective aspects. Experiments show that the proposed model fuses the source information fully and decreases noise cleanly.

关键词： Optical, image and video signal processing Filtering methods in signal processing computer vision and image processing techniques Patient diagnostic methods and instrumentation Biology and medical computing

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Bi-level image compression technique using neural networks

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IET image processing 2012年第5期6卷 496-506页

作者： Sahami, S. Shayesteh, M. G. Urmia Univ Dept Elect Engn Orumiyeh Iran Sharif Univ Technol Wireless Res Lab ACRI Dept Elect Engn Tehran Iran

This study presents the utilisation of neural-network for bi-level image compression. In the proposed lossy compression method, the locations of pixels of image are applied to the inputs of a multilayer perceptron neural-network. The output of the network denotes the pixel intensity (0 or 1). The final weights of the trained neural-network are quantised, represented by a few bits, Huffman encoded and then stored as the compressed image. In the decompression phase, by applying the pixels locations to the trained network, the output determines the intensity. The results of experiments on more than 4000 different images indicate higher compression rate of the proposed structure compared with the commonly used methods such as Comite Consultatif International Telephonique of Telegraphique (CCITT) G4 and joint bi-level image experts group (JBIG2) standards. Moreover, quantisation issue in neural-network deployment is addressed and a solution is proposed. Further, an adaptive technique based on binary image characteristics is applied to achieve more compression rates.

关键词： binary image characteristics Neural computing techniques JBIG2 standards Huffman encoding data compression data compression rates multilayer perceptrons lossy compression method computer vision and image processing techniques image and video coding image coding quantisation issue CCITT G4 quantisation (signal) trained neural-network joint bilevel image experts group standard adaptive technique multilayer perceptron neural-network bilevel image compression technique Comité Consultatif International Téléphonique of Télégraphique G4 Huffman codes decompression phase image pixel intensity

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A plexus-convolutional neural network framework for fast remote sensing image super-resolution in wavelet domain

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IET image processing 2021年第8期15卷 1679-1687页

作者： Deeba, Farah Zhou, Yuanchun Dharejo, Fayaz Ali Khan, Muhammad Ashfaq Das, Bhagwan Wang, Xuezhi Du, Yi Chinese Acad Sci Univ Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China Hamdard Univ Fac Engn Sci & Technol Karachi 72400 Pakistan Incheon Natl Univ IoT & Big Data Res Ctr Dept Elect Engn Incheon 22012 South Korea Quaid E Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah 67480 Pakistan

Satellite image processing has been widely used in recent years in a number of applications such as land classification, Identification transfer, resource exploration, super-resolution image, etc. Due to the orbital location, revision time, quick view angle limitations, and weather impact, the satellite images are challenging to manage. There are many types of resolution, such as spatial, spectral, and temporal. Still, in our case, we concentrated on spatial image resolution to super resolve the images from low-resolution images. For remote sensing image super-resolution fast wavelet-based super-resolution (FWSR), we propose a novel, fast wavelet-based plexus framework that performs super-resolution convolutional neural network (SRCNN)-like extraction of features based on three hidden layers. First, wavelet sub-band images are combined into a pre-defined full-scale data training factor, including approximation and interchangeable stand-alone units (frequency sub-bands). Second, to speed up image recovery, mapping the sub-band image of the wavelet is then measured using its approximate image. Third, the added sub-pixel layer at the end of the network model is intended to reproduce image quality using a plexus framework. The approximation sub-band images obtained after discrete wavelet transform wavelet decomposition are used as input rather than the original image because of their high-frequency data and preserved characteristics. Five current super-resolution neural network approaches are compared with the proposed technique and tested on three pubic satellite image datasets and two benchmark datasets. The experimental findings are well compared qualitatively and quantitatively.

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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Recognizing apple leaf diseases using a novel parallel real-time processing framework based on MASK RCNN and transfer learning: An application for smart agriculture

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IET image processing 2021年第10期15卷 2157-2168页

作者： Rehman, Zia Ur Khan, Muhammad Attique Ahmed, Fawad Damasevicius, Robertas Naqvi, Syed Rameez Nisar, Wasif Javed, Kashif HITEC Univ Taxila Dept Elect Engn Taxila Pakistan HITEC Univ Taxila Dept Comp Sci Taxila Pakistan Silesian Tech Univ Fac Appl Math Gliwice Poland COMSATS Univ Islamabad Dept Elect & Comp Engn Wah Campus Wah Cantt Pakistan COMSATS Univ Islamabad Dept Comp Sci Wah Campus Wah Cantt Pakistan SMME Nust Dept Robot Islamabad Pakistan

Effective recognition of fruit leaf diseases has a substantial impact on agro-based economies. Several fruit diseases exist that badly impact the yield and quality of fruits. A naked-eye inspection of an infected region is a difficult and tedious process;therefore, it is required to have an automated system for accurate recognition of the disease. It is widely understood that low contrast images affect identification and classification accuracy. Here a parallel framework for real-time apple leaf disease identification and classification is proposed. Initially, a hybrid contrast stretching method to increase the visual impact of an image is proposed and then the MASK RCNN is configured to detect the infected regions. In parallel, the enhanced images are utilized for training a pre-trained CNN model for features extraction. The Kapur's entropy along MSVM (EaMSVM) approach-based selection method is developed to select strong features for the final classification. The Plant Village dataset is employed for the experimental process and achieve the best accuracy of 96.6% on the ensemble subspace discriminant analysis (ESDA) classifier. A comparison with the previous techniques illustrates the superiority of the proposed framework.

关键词： Optical, image and video signal processing computer vision and image processing techniques Products and commodities Agriculture Other topics in statistics

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Agile reactive navigation for a non-holonomic mobile robot using a pixel processor array

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IET image processing 2021年第9期15卷 1883-1892页

作者： Liu, Yanan Bose, Laurie Greatwood, Colin Chen, Jianing Fan, Rui Richardson, Thomas Carey, Stephen J. Dudek, Piotr Mayol-Cuevas, Walterio Univ Bristol Bristol Robot Lab Bristol BS16 1QY Avon England Univ Bristol Visual Informat Lab Bristol Avon England Univ Manchester Dept Elect & Elect Engn Manchester Lancs England Univ Calif San Diego Dept Comp Sci & Engn San Diego CA 92103 USA Univ Calif San Diego Dept Ophthalmol San Diego CA 92103 USA Amazon Seattle WA USA

This paper presents an agile reactive navigation strategy for driving a non-holonomic ground vehicle around a pre-set course of gates in a cluttered environment using a low-cost processor array sensor. This enables machine vision tasks to be performed directly upon the sensor's image plane, rather than using a separate general-purpose computer. The authors demonstrate a small ground vehicle running through or avoiding multiple gates at high speed using minimal computational resources. To achieve this, target tracking algorithms are developed for the Pixel processing Array and captured images are then processed directly on the vision sensor acquiring target information for controlling the ground vehicle. The algorithm can run at up to 2000 fps outdoors and 200 fps at indoor illumination levels. Conducting image processing at the sensor level avoids the bottleneck of image transfer encountered in conventional sensors. The real-time performance of on-board image processing and robustness is validated through experiments. Experimental results demonstrate the algorithm's ability to enable a ground vehicle to navigate at an average speed of 2.20 m/s for passing through multiple gates and 3.88 m/s for a 'slalom' task in an environment featuring significant visual clutter.

关键词： Optical, image and video signal processing Spatial variables control Transportation system control Mobile robots Telerobotics computer vision and image processing techniques

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Adaptive comprehensive particle swarm optimisation-based functional-link neural network filtre model for denoising ultrasound images

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IET image processing 2021年第6期15卷 1232-1246页

作者： Kumar, Manish Mishra, Sudhansu Kumar Joseph, Justin Jangir, Sunil Kumar Goyal, Dinesh Mody Univ Sci & Technol Dept Elect & Biomed Engn Sikar 322311 India Birla Inst Technol Dept Elect & Elect Engn Ranchi Bihar India Indian Inst Technol Dept Elect Engn Gandhinagar India Poornima Inst Engn Technol Dept Comp Sci & Engn Jaipur Rajasthan India

Multiplicative speckle is a dominant type of noise that spoils the inherent features of the medical ultrasound (US) images. Apart from the speckle, impulse and Gaussian noises also appear in the US image due to the error encountered during the data transmission and transition of switching circuits and sensors. The noise not only deteriorates the visual quality of the US but also creates complications in the diagnosis. In this study, an adaptive comprehensive particle swarm optimisation-based functional-link neural network (ACPSO-FLNN) filtre has been proposed and implemented in filtering noisy US images in different noise conditions. The proposed filtre is compared with some state-of-the-art filtering techniques. Quantitative and qualitative measures such as training time, time complexity, convergence rate, and statistical test are included to study the performance of the proposed filtre. Furthermore, sensitivity, computational complexity, and order of the proposed filtre are also investigated. Friedman's test with 50 images is performed for statistical validation. The lower rank, that is, 6 and critical value of 21 x 10-4 of the proposed ACPSO-FLNN filtre validates its dominance over other filtres.

关键词： Sonic and ultrasonic radiation (medical uses) Patient diagnostic methods and instrumentation Other topics in statistics Optimisation techniques Optical, image and video signal processing Filtering methods in signal processing Sonic and ultrasonic radiation (biomedical imaging/measurement) Other topics in statistics Optimisation techniques computer vision and image processing techniques Biology and medical computing

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Full-reference tone-mapped images quality assessment

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IET image processing 2021年第3期15卷 763-773页

作者： Faraji, Mohammad Reza Inst Adv Studies Basic Sci Dept Comp Sci & Informat Technol Zanjan Iran

Various tone mapping operators have been proposed to convert the high dynamic range images to low dynamic ranges to improve visualization on low dynamic range displays. This paper presents a full-reference objective quality assessment index to evaluate the perceived quality of tone-mapped images. The proposed method seamlessly employs the multi-scale structural fidelity, statistical naturalness, colourfulness and the multi-scale free energy of the image to create a similarity score between a produced low dynamic range image and its high dynamic range image. The extensive experiments on three publicly available datasets using Spearman's rank-order correlation coefficient, Kendall's rank-order correlation coefficient and receiver operating characteristics analyses indicate the proposed tone-mapped quality index is superior to recently proposed state-of-the-art objective quality indices.

关键词： Optical, image and video signal processing computer vision and image processing techniques Other topics in statistics Other topics in statistics

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New multi-resolution image stitching with local and global alignment

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IET computer vision 2010年第4期4卷 231-246页

作者： Fang, X. Luo, B. Zhao, H. Tang, J. Zhai, S. Anhui Univ Key Lab Intelligent Comp & Signal Proc MOE Hefei 230039 Peoples R China

Three main problems affect the alignment quality of existing studies on multi-resolution image stitching: (i) the initial motion obtained is sometimes incorrect;(ii) the local motion is hard to be estimated and (iii) the widely used global bundle adjustment is difficult to converge. The authors propose a new multi-resolution image mosaic method that combines three corresponding tactics to solve these problems. The first problem is solved by introducing an additional motion refinement strategy, which consists of the low-contrast filter and RANSAC. The former removes flatly textured surface pixels and thus eliminates the falsely matched features. The latter removes outliers and finds a robust initial motion for the next layer. The second problem is resolved by a new iteratively local registration method, which calibrates the current camera parameters based on those from previous image with robust non-linear optimisation methods. It improves the convergence efficiency and eliminates error minimisation. For the last problem, the authors introduce a five-parameter bundle adjustment method based on the axis-angle decomposition of the rotation matrix. Comparing with existing bundle adjustment methods, this method is more stable because of an accurate and simple rotation decomposition. The authors show the efficiency of the method with qualitative and quantitative experiments.

关键词： local alignment image mosaic method multiresolution image stitching Resolution of optical images global alignment image processing and restoration Optical, image and video signal processing computer vision and image processing techniques five-parameter bundle adjustment method iteratively local registration method image resolution image segmentation

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Multistage reaction-diffusion equation network for image super-resolution

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IET image processing 2021年第12期15卷 2926-2936页

作者： Pu, Xiaofeng Wang, Zengmao Wuhan Univ Sch Comp Sci Wuhan Peoples R China

Deep learning-based models have progressed considerably in single-image super-resolution. A high-resolution pattern generation task is performed at the end of convolution neural networks (CNNs) with some convolution-based operations in these models. However, this process may be difficult because all the work is done through the remarkable learning ability of CNN without any specific learning target. Reaction-diffusion equation (RDE) is a mechanism involved in the pattern generation process that can serve as a guide for super-resolution. It is proposed to embed RDE into a super-resolution network by designing a reaction-diffusion process block (RDPB) in this study. The proposed RDPB uses Euler method for iteratively solving one particular RDE, which is determined by the parameter generated through CNN. Accordingly, this module guides and leads the CNN in generating patterns for image super-resolution. Moreover, a multistage framework is constructed to guide each network module further. On the basis of these two designs, the multistage reaction-diffusion equation network is proposed for image super-resolution. Experimental results demonstrated that the proposed model can obtain findings consistent with the conclusions of state-of-the-art methods with a relatively shallow structure and small model size.

关键词： Optical, image and video signal processing computer vision and image processing techniques Neural nets

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