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Atomic wavelets in lossy and near lossless image compression 26

Atomic wavelets in lossy and near lossless image compression

引用

conference on image and signal processing for Remote Sensing xxVI

作者： Makarichev, Viktor O. Lukin, Vladimir V. Brysina, Iryna, V Vozel, Benoit Chehdi, Kacem Natl Aerosp Univ UA-61070 Kharkov Ukraine Univ Rennes 1 IETR UMR CNRS 6164 F-22305 Lannion France

ISBN: (纸本)9781510638808

One of distinguishing features of the present is the explosive increase in data amount including digital images such as satellite remote sensing images. processing, storing and transmission via networks of a huge number of digital images requires considerable resources in the sense of memory, time, computational power, etc. In this regard, the importance of image compression is growing and the development of novel compression techniques that would satisfy new requirements, for instance, to security and level of privacy protection continues. Atomic wavelets and their generalizations can be a useful tool for this. They are constructed using atomic functions, which are compactly supported solutions of special functional differential equations. Discrete atomic transform (DAT), which is a process of computation of expansion coefficients of the function describing the source discrete data, is applied in discrete atomic compression (DAC). DAC can be applied to compression of full color digital images, as well as monocomponent ones. In this paper, we investigate efficiency, which is measured by compression ratio (CR), of satellite image compression using DAC and the corresponding loss of quality measured by several quantitative criteria. They are maximum absolute deviation (MAD), root mean square (RMS) and peak signal-to-noise ratio (PSNR). We show that DAC provides near lossless compression, when quality loss is minor in a sense of the MAD-metric. Also, it is proved that using DAC it is possible to obtain better compression than by applying JPEG with the same quality of the obtained results.

关键词： image compression wavelets Computing systems image quality Digital imaging Quality measurement Visual compression Visualization Earth observing sensors Satellite imaging

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POPs: Pluck Out the Petals

POPs: Pluck Out the Petals

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International conference on signal processing and Communications, SPCOM

作者： Shashikant Verma Subramanian Sankaranarayanan Shanmuganathan Raman

ISBN: (数字)9798350350456

ISBN: (纸本)9798350350463

In this work, we present a method for precisely segmenting individual petals within flower images. Petal segmentation is challenging due to its intricate structures and substantial variations among petals across diverse flower species. Moreover, frequent occlusions and the uniformity in appearance and shape of petals within a single flower add complexity to the segmentation process, making it error-prone. We introduce a generalized approach to segment petal instances for any flower species. We first detect contours for the outermost set of petals and segment them out from the Corolla. We iteratively repeat this process until we reach the floral centre. To this end, we developed a robust contour detection technique and employed a prompt-based segmentation model for petal segmentation. Our results demonstrate the effectiveness of prompts generated by our approach in facilitating efficient petal segmentation. Additionally, we have developed a manual supervision tool, FloraSeg, tailored for segmenting petals in cases where flowers exhibit many petals with complex geometries. Our experiments demonstrate that the fully automatic segmentation method achieves results comparable to those obtained through manual human annotation. Addition-ally, incorporating a few manual cues from the FloraSeg tool into our approach consistently yields precise petal segmentation, aligning closely with manually annotated outcomes. We present results from a wide range of flower species to emphasize the effectiveness of the proposed approach in the petal segmentation task. This work has significant applications in developmental biology, particularly in advancing our understanding of flower development and conducting structural phenotyping of flowers.

关键词： image segmentation Annotations Shape Symbols signal processing algorithms Manuals Flowering plants

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LiSenNet: Lightweight Sub-band and Dual-Path Modeling for Real-Time Speech Enhancement

LiSenNet: Lightweight Sub-band and Dual-Path Modeling for Re...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haoyin Yan Jie Zhang Cunhang Fan Yeping Zhou Peiqi Liu NERC-SLIP University of Science and Technology of China (USTC) Hefei China Anhui Province Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei China China Merchants Bank (CMB) Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech enhancement (SE) aims to extract the clean waveform from noise-contaminated measurements to improve the speech quality and intelligibility. Although learning-based methods can perform much better than traditional counterparts, the large computational complexity and model size heavily limit the deployment on latency-sensitive and low-resource edge devices. In this work, we propose a lightweight SE network (LiSenNet) for real-time applications. We design sub-band downsampling and upsampling blocks and a dual-path recurrent module to capture band-aware features and time-frequency patterns, respectively. A noise detector is developed to detect noisy regions in order to perform SE adaptively and save computational costs. Compared to recent higher-resource-dependent baseline models, the proposed LiSenNet can achieve a competitive performance with only 37k parameters (half of the state-of-the-art model) and 56M multiply-accumulate (MAC) operations per second.

关键词： Performance evaluation Adaptation models Time-frequency analysis Computational modeling image edge detection Noise Detectors Speech enhancement Real-time systems Noise measurement

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Fault detection based on Principal Component Analysis in the context of Missing Data

Fault detection based on Principal Component Analysis in the...

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International conference on Control, Automation and Diagnosis (ICCAD)

作者： Yosra Fatnassi Ines Jaffel Mohamed Guerfel Nasreddine Bouguila Laboratory of Automatic Signal and Image Processing (LARATSI) National School of Engineers of Monastir University of Monastir Tunisia Higher Institute of Applied Sciences and Technology of Kairouan Universiy of Kairouan Tunisia Higher Institute of Applied Sciences and Technology of Sousse Universiy of Sousse Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Improving fault detection and diagnosis (FDD) in systems with incomplete features is a prevalent challenge in industrial applications. In this context, two distinct approaches (k-means clustering and k-nearest neighbor imputation KNN) for addressing Missing Data (MD) were presented and compared. The evaluation was conducted with introducing of MD, ranging from a 10% to 50%. The proposals aim to enhance the monitoring and diagnosis of the system through the utilization of principal component analysis (PCA) for feature extraction an fault detection.

关键词： Error analysis Fault detection Feature extraction Chemical reactions Imputation Reliability Proposals

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Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks

Revolutionizing Disease Diagnosis with simultaneous function...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Luoyu Wang Yitian Tao Qing Yang Yan Liang Siwei Liu Hongcheng Shi Dinggang Shen Han Zhang School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Nuclear Medicine Zhongshan Hospital Shanghai China State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China Shanghai Clinical Research and Trial Center Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Simultaneous functional PET/MR (sf-PET/MR) presents a cutting-edge multimodal neuroimaging technique. It provides an unprecedented opportunity for concurrently monitoring and integrating multifaceted brain networks built by spatiotemporally covaried metabolic activity, neural activity, and cerebral blood flow (perfusion). Albeit high scientific/clinical values, short in hardware accessibility of PET/MR hinders its applications, let alone modern AI-based PET/MR fusion models. Our objective is to develop a clinically feasible AI-based disease diagnosis model trained on comprehensive sf-PET/MR data with the power of, during inferencing, allowing single modality input (e.g., PET only) as well as enforcing multimodal-based accuracy. To this end, we propose Mx-ARM, a multimodal Mixture-of-experts Alignment and Reconstruction Model. It is modality detachable and exchangeable, allocating different multi-layer perceptrons dynamically ("mixture of experts") through learnable weights to learn respective representations from different modalities. Such design will not sacrifice model performance in uni-modal situation. To fully exploit the inherent complex and nonlinear relation among modalities while producing fine-grained representations for uni-modal inference, a modal alignment module is utilized to line up a dominant modality (e.g., PET) with representations of auxiliary modalities (MR). We further adopt multimodal reconstruction to promote the quality of learned features. Experiments on precious multimodal sf-PET/MR data for Mild Cognitive Impairment diagnosis showcase the efficacy of Mx-ARM toward clinically feasible precision medicine.

关键词： Accuracy Precision medicine signal processing Brain modeling Resonance Hemodynamics Spatiotemporal phenomena Medical diagnosis Speech processing image reconstruction

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A Comparative Study of Deep Learning Models for image Super-Resolution Across Various Magnification Levels

A Comparative Study of Deep Learning Models for Image Super-...

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Future Machine Learning and Data Science (FMLDS), IEEE International conference on

作者： Jayesh Soni Subhash Gurappa Himanshu Upadhyay Applied Research Center Florida International University Miami USA Department of Electrical and Computer Engineering Florida International University Miami USA

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

image Super-Resolution (SR) is a vital image processing technique aimed at improving the resolution of lowresolution images to generate high-resolution images, which is essential in applications such as medical imaging, forensic analysis, satellite imagery, etc. Despite its significance, there are still issues with existing SR techniques, such as handling unknown degradation, generalizing across various domains, and recovering the fine details, especially at high scaling factors. Traditional approaches like interpolation and reconstruction-based methods produce poor results, which leads to the use of deep learning models that have demonstrated notable advancements. However, problems like computational complexity, performance drop on real-world data, and dependency on fixed scaling factors remain. In this research, we conduct a comparative analysis of SR models, namely Super-Resolution Convolutional Neural Networks (SRCNN), SwinIR, and Real-ESRGAN, across multiple image domains (e.g., Animals, Cars, Faces, Humans, Landscapes, Ships) using different scaling factors (e.g., $x 2, x 3, x 4$ ) and metrics like Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), Learned Perceptual image Patch Similarity (LPIPS), Objective Difference image (ODI) Score, Tenengrad sharpness and Laplacian Variance. This approach helps us better understand the performance of various deep-learning models, guiding further improvements to ensure they are efficient, robust, and applicable to real-world scenarios.

关键词： Measurement Deep learning Analytical models Visualization PSNR Laplace equations Computational modeling Superresolution Indexes Automobiles

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Dental x-ray image Segmentation using Octave Convolution Neural Network 28

Dental X-ray Image Segmentation using Octave Convolution Neu...

引用

28th signal processing and Communications applications conference (SIU)

作者： Kaya, Mete Can Akar, Gozde Bozdagi Orta Dogu Tekn Univ Elekt & Elekt Muhendisligi Ankara Turkey

ISBN: (纸本)9781728172064

In this paper, we present a Unet architecture made of octave convolution for dental image segmentation problem. In this architecture, the requirements for memory and accuracy are significantly improved compared to previous works in the literature. Compare to state-of-art models on this topic the classification accuracy in dental image segmentation is increased by %2, and the memory usage is decreased by %70. Suggested architecture showed a performance of success on 15B12015 dataset.

关键词： Unet Octave Convolution CNN Focal Loss Dental x-ray image Segmentation

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FBNet: Feature Balance Network for Urban-Scene Segmentation

FBNet: Feature Balance Network for Urban-Scene Segmentation

引用

2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Gan, Lei Huang, Huabin Li, Banghuai Yuan, Ye Beihang University Beijing China Megvii Inc Beijing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

image segmentation in the urban scene has recently attracted much attention due to its success in autonomous driving sys-tems. However, the poor performance of concerned fore-ground targets, e.g., traffic lights and poles, still limits its further practical applications. In urban scenes, foreground targets are always concealed in their surrounding stuff be-cause of the special camera position and 3D perspective pro-jection. What's worse, it exacerbates the imbalance between foreground and background classes in high-level features due to the continuous expansion of the receptive field. We call it Feature Camouflage. In this paper, we present a novel add-on module, named Feature Balance Network (FBNet), to eliminate the feature camouflage in urban-scene segmen-tation. FBNet consists of two key components, i.e., Block-wise BCE(BwBCE) and Dual Feature Modulator(DFM). Bw-BCE serves as an auxiliary loss to ensure uniform gradients for foreground classes and their surroundings during back-propagation. At the same time, DFM intends to enhance the deep representation of foreground classes in high-level features adaptively under the supervision of BwBCE. These two modules facilitate each other as a whole to ease feature cam-ouflage effectively. Our proposed method achieves a new state-of-the-art segmentation performance on two challenging urban-scene benchmarks, i.e., Cityscapes and BDD100K. Code will be released for reproduction. © 2022 IEEE.

关键词： image segmentation

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Resolution enhanced partial aperture imaging system using annular coded phase reflectors 35

Resolution enhanced partial aperture imaging system using an...

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conference on Practical Holography xxxV - Displays, Materials, and applications

作者： Bulbul, Angika Rosen, Joseph Ben Gurion Univ Negev Sch Elect & Comp Engn POB 653 IL-8410501 Beer Sheva Israel

ISBN: (数字)9781510642560

ISBN: (纸本)9781510642560

Imaging with partial apertures is suitable for applications where compact, light-weight, and cost-effective optical systems are desired. The feasibility of a partial aperture imaging system (PAIS) with a single annular coded-phase aperture was first studied for future space and ground-based telescopic systems. The concept of PAIS is based on the interferenceless coded aperture correlation holography (I-COACH) technique. Since I-COACH is an incoherent and interferenceless technique, it is compatible with telescopic applications. Although, in PAIS, only a small fraction of light is modulated by the coded aperture the image resolution of PAIS is similar to that of full-aperture imaging systems. In this study, the design and the reconstruction technique of PAIS are modified to provide higher image quality with improved resolution, lower noise, and higher visibility. The upgraded design makes use of sparse-response holograms and a nonlinear reconstruction. Far-field imaging usually suffers from lower power signals at the sensor plane, causing a lower signal-to-noise ratio (SNR) and degradation in the image quality. To improve the SNR, it is crucial to use a power-efficient system, with higher signal intensity per camera pixel. This goal can be achieved by accumulating all the incoming power to a small sensor area, and distributing the light between several randomly distributed dots on the sensor, instead of inefficiently spreading the light over the entire camera plane. The results of the modified PAIS (M-PAIS) are reconstructed by a nonlinear correlation to provide well-resolved images which are compared with full aperture direct imaging results.

关键词： Digital holography Coded aperture imaging Imaging & signal processing Telescopes Imaging systems

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Framing photos in the digital dark age: towards a socio-technological 'ecology of images' 44

Framing photos in the digital dark age: towards a socio-tech...

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conference applications of Digital image processing xLIV

作者： Caldwell, S. Gedeon, T. Australian Natl Univ Sch Comp Acton ACT 2601 Australia

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

The binary question of photography as science or art has been raging since Daguerre invented silver halide photography in 1839. Never has this controversy been more crucial, or been raised to such a fever pitch as in today's socio-technological world, in which uncertain images are consumed like candy and the evidentiary status of photographs is precarious at best. The 'offline' society of last century is behind us, where a philosopher like Sontag could confidently state that "a photograph passes for incontrovertible proof that something exists, or did exist, which is like what's in the picture" [1]. The 'ecology of images' of which she spoke is urgently needed now if we are to establish a framework within which the daily onslaught of uncertain imagery can be understood, the invidious action of image-based partisan persuasion can be combatted, pioneering achievements in image artistry can be celebrated, and a reliable photographic record of our current and future real world can be preserved. We may be better positioned now than at any time in the past to attempt such a task. Imaging technologies (ubiquitous digital photography, sophisticated camera sensors, image editing, synthetic image generation) have brought to fruition the use of photographic images to expand into a wide range of 'ecological' niches with varying impacts on individuals and societies. Other technologies (physiological signal processing, artificial intelligence), provide us with the means to gain insight into human perspectives and conscious/subliminal perceptions of these images. The contexts that apply to image distribution (digital platforms, social engineering, governmental regulation, communities of interest, commercial interests) are daily news. Early attempts to address image integrity (authenticity initiatives, fact-checking organisations) highlight the desire of humans to illuminate the age in which we find ourselves, a 'digital dark ages' [2] of eroding authenticity of our traditional kno

关键词： ecology of images image credibility human perspectives of images digital platforms

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