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Improved decision-based detail-preserving variational method for removal of random-valued impulse noise

IET image processing 2012年第7期6卷 976-985页

作者： Zhou, Y. Y. Ye, Z. F. Huang, J. J. Univ Sci & Technol China Dept Elect Engn & Informat Sci Inst Stat Signal Proc Hefei 230027 Anhui Peoples R China

The authors propose an improved decision-based detail-preserving variational method (DPVM) for removal of random-valued impulse noise. In the denoising scheme, adaptive centre weighted median filter (ACWMF) is first ameliorated by employing the variable window technique to improve its detection ability in highly corrupted images. Based on the improved ACWMF, a fast iteration strategy is used to classify the noise candidates and label them with different noise marks. Then, all the noise candidates are restored one-time by weight-adjustable detail-preserving variational method. The weights between the data-fidelity term and the smooth regularisation term of the convex cost-function in DPVM are decided by the noise marks. After minimisation, the restored image is obtained. Extensive simulation results show that the proposed method outperforms some existing algorithms, both in vision and quantitative measurements. Moreover, our method is faster than some decision-based DPVM. Therefore it can be ported into practical application easily.

关键词： iterative methods Filtering methods in signal processing data fidelity term random valued impulse noise removal highly corrupted images decision based detail preserving variational method computer vision and image processing techniques image restoration impulse noise variable window technique Optical, image and video signal processing median filters convex cost function detection ability adaptive centre weighted median filter adaptive filters noise marks Interpolation and function approximation (numerical analysis) ACWMF smooth regularisation term noise candidates image denoising iteration strategy

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image enhancement by wavelet-based thresholding neural network with adaptive learning rate

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IET image processing 2011年第7期5卷 573-582页

作者： Bhutada, G. G. Anand, R. S. Saxena, S. C. Indian Inst Technol Roorkee Dept Elect Engn Roorkee Uttar Pradesh India

A new approach has been proposed to improve the computational performance of denoising in which adaptively defined learning step size has been used for tuning the parameter of the thresholding function of wavelet transform-based thresholding neural network (WT-TNN) methodology. In this approach, steepest gradient-based learning step size of WT-TNN methodology are changed to the proposed adaptively defined learning step size for tuning the parameters of thresholding function. The results of the image enhanced by such adaptive learning step size exhibit the increase in the speed of learning and improved edge preservation feature. Further, the learning time has also become independent of noise level and initial values of learning parameters.

关键词： edge detection Neural computing techniques WT-TNN methodology image denoising computer vision and image processing techniques gradient-based learning step size wavelet-based thresholding neural network adaptive learning rate telecommunication computing Optical, image and video signal processing image enhancement edge preservation feature neural nets

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MPGI-Terminal defect detection based on M-FRCNN

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IET image processing 2023年第2期17卷 428-438页

作者： Liu, Qunpo Wang, Mengke Wang, Haixing Hanajima, Naohiko Henan Polytech Univ Sch Elect Engn & Automat 2001 Century Ave Jiaozuo 454003 Henan Peoples R China Henan Int Joint Lab Direct Drive & Control Intell 2001 Century Ave Jiaozuo Henan Peoples R China Muroran Inst Technol Coll Informat & Syst 27-1 Mizumoto Cho Muroran Hokkaido Japan

Micro-precision glass insulated terminal (MPGI-Terminal) is a high-precision and widely used micro-precision component. The MPGI-Terminal is small in size with 2-5 mm in diameter. It is difficult to distinguish missing blocks on the surface during manual inspection, which is prone to false detection and missing detection. The advantages of deep learning methods in the field of object detection have gradually highlighted. They can be used to construct the missing block detection model of MPGI-Terminal. A Max-Min Intersection over Union (MIoU) loss function based on overlap area, distance and aspect ratio is proposed, which helps the model to perform bounding box regression more reasonably, so as to obtain the object position more accurately. In addition, the Spatial Attention Module (SAM) and the Region of Interest Alignment (RoI Align) module are introduced to further improve the model performance. Based on the above modules, a MPGI-Terminal missing block detection model based on M-FRCNN is constructed. The results show that the proposed model can meet the requirements of fast and high accuracy of MPGI-Terminal missing block detection.

关键词： MPGI-Terminal missing block detection model missing detection image segmentation feature extraction Regression analysis object detection false detection deep learning (artificial intelligence) Neural nets missing blocks Optical, image and video signal processing regression analysis microprecision glass insulated terminal microprecision component MPGI-Terminal defect detection computer vision and image processing techniques inspection convolutional neural nets

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Effective algorithm for detecting various wipe patterns and discriminating wipe from object and camera motion

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IET image processing 2010年第6期4卷 429-442页

作者： Warhade, K. K. Merchant, S. N. Desai, U. B. Indian Inst Technol Dept Elect Engn SPANN Lab Bombay 400076 Maharashtra India

Wipe transition detection in video segmentation is more difficult to detect than abrupt and other gradual transitions, because of diversity in patterns, and difficulty in distinguishing wipe from camera and object motion. In this study, the authors propose an algorithm for wipe transition detection. In the proposed algorithm, first the moving strip due to wipe is obtained, which eliminate most of the edges due to object boundaries and retain true wipe boundaries, and then Hough transform is applied on these moving lines to detect and categorise various wipe types. In order to decrease the computational load of the proposed algorithm, the authors propose a preprocessing step as a first stage of the algorithm. The preprocessing step consists of calculation of statistical image difference between the consecutive frames to obtain the potential wipe frames, which are input to their proposed algorithm. The proposed algorithm detects and identifies various types of wipes and also distinguishes wipes from object and camera motion. The performance comparison of the proposed algorithm, with and without preprocessing, with other existing techniques clearly exhibit its effectiveness in terms of better recall, precision, F1 measure and detection rate.

关键词： wipe pattern detection wipe transition detection image segmentation camera motion statistical analysis image sensors computer vision and image processing techniques statistical image difference calculation true wipe boundary object detection Other topics in statistics Video signal processing Optical, image and video signal processing image motion analysis object motion video segmentation cameras

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Real-time pedestrian classification exploiting 2D and 3D information

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IET INTELLIGENT TRANSPORT SYSTEMS 2008年第3期2卷 201-210页

作者： Corneliu, T. Nedevschi, S. Tech Univ Cluj Napoca Dept Comp Sci Cluj Napoca 400020 Romania

A new approach for standing and walking pedestrian detection using pattern matching and exploiting both 2D image information and 3D dense stereo information is proposed. Because 3D information accuracy does not allow the direct classification of the 3D shape, a combined 3D-2D method is proposed. The 3D data are used in an innovative way for pedestrian hypotheses generation, scale and depth estimation and 2D models selection. Also the 3D hypotheses allow the corresponding 2D image region of interest selection and the 2D hypothesis generation. The 2D hypothesis consists of the object's external edges obtained by an edge extraction and a depth coherency-based filtering out process. The scaled models are matched against the selected hypothesis using an elastic high-speed matching based on the Chamfer distance. The method has been tested on synthetic and real-world scenarios.

关键词： edge detection image recognition 3D dense stereo information real-time pedestrian classification pattern matching computer vision and image processing techniques 3D information Chamfer distance stereo image processing depth coherency-based filtering elastic high-speed matching filtering theory 2D information edge extraction pedestrian detection

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Error resilient video coding with priority data classification using H.264 flexible macroblock ordering

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IET image processing 2007年第2期1卷 197-204页

作者： Im, S. K. Pearmain, A. J. Univ London Queen Mary Coll Dept Elect Engn London E1 4NS England

Flexible macroblock ordering (FMO) is a new error resilience feature in the H.264 (MPEG-4 part 10) video coding standard. The paper exploits FMO to offer a new classification algorithm for prioritised video transmission. Instead of using the default mapping structures of FMO, an optimisation algorithm in which the more important macroblocks (MBs) are categorised in a separate slice group (SG), which corresponds to high-priority packets for transmission, is proposed. The importance of each MB is determined based on its eventual influence on picture quality. This is assessed by considering the fact that the successful transmission of an MB not only enhances the quality of the associated pixels, but also improves the quality of its adjacent lost MBs by improving the efficiency of error concealment. It is assumed that the network can offer a prioritised service for successful transmission of the high-priority SG. Based on this, a vulnerability factor for each MB is determined, and a certain proportion of high-vulnerability MBs at each frame to limit the impact of temporal error propagation is intra-updated. It is shown, where the proposed mapping algorithm outperforms the default mappings of the H.264 codec, that this prioritised transmission will improve the subjective and objective video quality in situations with a high probability of transmission errors.

关键词： prioritised video transmission priority data classification mapping algorithm H.264 flexible macroblock ordering FMO computer vision and image processing techniques image and video coding error propagation video coding optimisation algorithm image classification video coding standard picture quality Video signal processing MPEG-4 part 10 slice group error resilient video coding vulnerability factor codec

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MSNet: A novel end-to-end single image dehazing network with multiple inter-scale dense skip-connections

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IET image processing 2021年第1期15卷 143-154页

作者： Yi, Qiaosi Jiang, Aiwen Deng, Xiaolin Liu, Changhong Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang Jiangxi Peoples R China

Dehazing is a challenging ill-posed image restoration task. Various prior-based and learning-based methods have been proposed. Among them, end-to-end deep models achieve great success on performance improvement. However, most of them are concentrated on feature learning within the same block scale in isolation, and cannot perform associated analysis well on feature characteristics of different scales. Inter-scale information reuse which is especially beneficial to image restoration is often neglected. Therefore, in this paper, a novel end-to-end network with multiple inter-scale dense skip-connections for image dehazing is proposed. Sufficient complementary information combination is considered through dense inter-scale skip-connections among encoder and decoder block layers. Besides avoiding gradient vanishing, a kind of bottleneck residual block is proposed to control the importance of local gradients at different scales over global learning process. Extensive comparisons and ablation studies on public dehazing datasets and real-world images have been conducted. The experiment results demonstrate that the proposed novel elements can ensure more stable training process and superior testing performance with great improvements on PSNR and SSIM. Authors' haze-removal results consistently comply satisfactorily with real situations, having much higher definition and contrast without colour distortion than those from the state-of-the-art methods compared in this paper.

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

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image compression using wavelets and JPEG2000: a tutorial

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ELECTRONICS & COMMUNICATION ENGINEERING JOURNAL 2002年第3期14卷 112-121页

作者： Lawson, S Zhu, J Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England

The demand for higher and higher quality images transmitted quickly over the Internet has led to a strong need to develop better algorithms for the filtering and coding of such images. The introduction of the JPEG2000 compression standard has meant that for the first time the discrete wavelet transform (DWT) is to be used for the decomposition and reconstruction of images together with an efficient coding scheme. The use of wavelets implies the use of subband coding in which the image is iteratively decomposed into high- and low-frequency bands. Thus there is a need for filter pairs at both the analysis and synthesis stages. This paper aims in tutorial form to introduce the DWT, to illustrate its link with filters and filterbanks and to illustrate how it may be used as part of an image coding algorithm. It concludes with a look at the qualitative differences between images coded using JPEG2000 and those coded using the existing JPEG standard.

关键词： Integral transforms in numerical analysis tutorial image decomposition filtering subband coding high-frequency bands discrete wavelet transform filtering theory data compression Internet JPEG2000 FIR filters image coding low-frequency bands computer vision and image processing techniques image compression filterbanks image reconstruction channel bank filters transform coding discrete wavelet transforms image and video coding Filtering methods in signal processing DWT

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Hand vein biometry based on geometry and appearance methods

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IET computer vision 2011年第6期5卷 398-406页

作者： Yuksel, A. Akarun, L. Sankur, B. Bogazici Univ Dept Comp Engn Istanbul Turkey Bogazici Univ Dept Elect & Elect Engn Istanbul Turkey

Many biometric systems, such as face, fingerprint and iris have been studied extensively for personal verification and identification purposes. Biometric identification with vein patterns is a more recent approach that uses the vast network of blood vessels underneath a person's skin. These patterns in the hands are assumed to be unique to each individual and they do not change over time except in size. As veins are under the skin and have a wealth of differentiating features, an attempt to copy an identity is extremely difficult. These properties of uniqueness, stability and strong immunity to forgery of the vein patterns make it a potentially good biometric trait which offers greater security and reliable features for personal identification. In this study, the authors present a novel hand vein database and a biometric technique based on the statistical processing of the hand vein patterns. The BOSPHORUS hand vein database has been collected under realistic conditions in that subjects had to undergo the procedures of holding a bag, pressing an elastic ball and cooling with ice, all exercises that force changes in the vein patterns. The applied recognition techniques are a combination of geometric and appearance-based techniques and good identification performances have been obtained on the database.

关键词： Spatial and pictorial databases visual databases vein recognition biometric trait biometric identification hand vein biometry BOSPHORUS hand vein database hand vein pattern statistical processing geometric technique appearance methods computer vision and image processing techniques image recognition blood vessels

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Co-teaching based pseudo label refinery for cross-domain object detection

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IET image processing 2021年第13期15卷 3189-3199页

作者： Wang, Kunpeng Cai, Jingxiang Yao, Juan Liu, Peng Zhu, Zhiqin Southwest Univ Sci & Technol Sch Informat & Engn Mianyang 621010 Sichuan Peoples R China Univ Sci & Technol China Dept Automat Hefei Peoples R China Chongqing Univ Posts & Telecommun Coll Automat Chongqing Peoples R China

Object detection is one of the main tasks in computer vision and has made great progress in recent years. However, the performance of target detectors is significantly dropped by the differences between existing datasets and application scenarios, leading to the so-called domain shift problem. To address such an issue, a novel co-teaching based pseudo label refinery framework for cross-domain object detection is developed, which cooperates with two models to select data from target domain for each other. This strategy can effectively purify the predicted pseudo labels and resist noisy labels. Specifically, the framework consists of two encoders (i.e. structure encoder and global encoder), two classifiers and one discriminator, in which structure encoder is used to extract structural features that are not disturbed by colour, and the global encoder is used to extract the complete discriminant features. The two encoders are each followed by a classifier. In training, the structure and global encoder with labelled source samples are first trained, so that it has the initial recognition ability. Then the samples assigned are used with pseudo labels by the classifier following the structure encoder to fine-tune the global encoder which pre-trained on the labelled source domain and obtain the refined labels for the target data. With the refined labels, the structure encoder is further optimised on the target domain. During this process, the proposal is to cross use the two classifiers to promote the mutual transfer of complementary capabilities of the two encoders. Moreover, a novel residual channel attention block (RCA) embedded with salient features is designed to pay more attention to the target regions. Extensive experiments demonstrate that the developed framework can generate clean labels for unlabelled target data and boost the performance of cross domain object detection. The code is available at .

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

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