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image denoising using block matching and convolutional neural network

JOURNAL OF ELECTRONIC IMAGING 2022年第4期31卷 041218-041218页

作者： Selvanambi, Ramani Victor, Akila Arunkumar, Gurunathan Kannadasan, Rajendran Sarawathi, Elumalai Rajkumar, Soundrapandiyan Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

image noise removal (denoising) is a significant preprocessing step in image processing research domain. In past decades, various noise removal algorithms have been developed, and they are categorized into conventional and deep learning denoising methods. Most recently, deep learning-based denoising methods outperform the conventional methods. However, deep learning methods have the limitations of requiring large training sample images, difficult to train the deep convolution neural network (DeCNN), and the majority of the convolutional neural network suffers from the performance saturation. Hence, using combination of block matching (BM) and dilated convolutional neural network (DCNN), a unique method for reducing noise in still images is proposed. In this approach, initially, existing algorithm used to preprocess the image. Then, BM technique uses a sliding window size of a four-by-four block and moves it across the image to select for blocks that are similar in the image. The matching blocks are highly correlated with one another. The matching blocks are then fed to the deep DCNN to remove noise and obtain a better noiseless image. Finally, the noiseless image performances are evaluated using standard metrics. From the experimental results, it is observed that the proposed BM and deep DCNN yielded substantially better results than existing state-of-the-art methods to remove the image's noise.

关键词： image denoising block matching stochastic gradient decent aggregation deep convolution neural network Gaussian noise peak signal-to-noise ratio dilated convolution batch normalization

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Are Standard CNNs Good Enough for No-Reference Stereoscopic image Quality Assessment?

Are Standard CNNs Good Enough for No-Reference Stereoscopic ...

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International Conference on signal processing and Communications, SPCOM

作者： Ishita Bardhan Sumohana Channappayya Abhik Banerjee Abdur Rahim Mohammad Forkan Prem Jayaraman Abhinav Kumar Dept. of Artificial Intelligence Indian Institute of Technology Hyderabad India Dept. of Computing Technologies Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798350350456

ISBN: (纸本)9798350350463

Perceptual quality metrics derived from deep features have led to a boost in modelling the Human Visual System (HVS) to perceive the quality of visual content. In this work, we study the effectiveness of fine-tuning three standard convolutional neural networks (CNNs) viz. ResNet50, VGG16 and MobileNetV2 to predict the quality of stereoscopic images in the no-reference setting. This work also aims to understand the impact of using disparity maps for quality prediction. Interestingly, our experiments demonstrate that disparity maps do not significantly contribute to improving perceptual quality estimation in the deep learning framework. To the best of our knowledge, this is the first study that explores the impact of disparity along with the chosen models for Stereoscopic image Quality Assessment. We present a detailed study of our experiments with various architectural configurations on the LIVE Phase I and ii datasets. Further, our results demonstrate the innate capability of deep features for quality prediction. Finally, the simple fine-tuning of the models results in solutions that compete with state-of-the-art patch-based stereoscopic image quality assessment methods.

关键词： image quality Measurement Deep learning Visualization Stereo image processing Estimation Visual systems

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1st International Conference on Advances in signal processing and Communication Engineering, ICASPACE 2021

1st International Conference on Advances in Signal Processin...

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1st International Conference on Advances in signal processing and Communication Engineering, ICASPACE 2021

ISBN: (纸本)9789811955495

The proceedings contain 44 papers. The special focus in this conference is on Advances in signal processing and Communication Engineering. The topics include: Single-Precision Floating-Point Multiplier Design Using Quantum-Dot Cellular Automata with Power Dissipation Analysis;compression Techniques for Low Power Hardware Accelerator Design: Case Studies;sequence Set Design for Radar System;design of an All Digital Phase-Locked Loop Using Cordic Algorithm;analysis of Deep Learning Algorithms for image Denoising;an Extensive Survey on Assessment of Multicore Processors for Embedded Systems;image Segmentation Techniques and Optimization Algorithms for Lung Cancer Detection;handwritten to Text Document Converter;An Efficient Energy Aware for Reliable Route Discovery Using Energy with Movement Detection Technique in MANET;Lumped Circuit Modeling at Nanoscale (Part-ii: Coupling Between Two Nanospheres);design and Implementation of Imprecise Adders for Low-Power Applications;speech Processed Public Addressing System;an Approach Towards Data Privacy Issues in Distributed Cyber Physical System;Classification of LPI Radar signals Using Multilayer Perceptron (MLP) neural Networks;a Systematic Review on Screening of Diabetic Retinopathy and Maculopathy Using Artificial Intelligence;Area Efficient and High-Throughput Radix-4 1024-Point FFT Processor for DSP Applications;air Quality Monitoring System Based on Artificial Intelligence;an Improved Technique for Identification of Forgery image Detection Using Clustering Method;EEG signal Analysis During Stroop Task for Checking the Effect of Sleep Deprivation;UWB Localization Procedures with Range Control methods—A Review;deep Learning Model for Multiclass Classification of Diabetic Retinal Fundus images Using Gradient Descent Optimization;multilevel Authentication to Wireless Sensor Networks Against Malicious Attacks Using Butterfly Method.

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NA-SODINN: A deep learning algorithm for exoplanet image detection based on residual noise regimes

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ASTRONOMY & ASTROPHYSICS 2023年 680卷

作者： Cantero, C. Absil, O. Dahlqvist, C. -H. Van Droogenbroeck, M. Univ Liege Montefiore Inst B-4000 Liege Belgium Univ Liege STAR Inst Allee 6 Aout 19C B-4000 Liege Belgium

Context. Supervised deep learning was recently introduced in high-contrast imaging (HCI) through the SODINN algorithm, a convolutional neural network designed for exoplanet detection in angular differential imaging (ADI) datasets. The benchmarking of HCI algorithms within the Exoplanet Imaging Data Challenge (EIDC) showed that (i) SODINN can produce a high number of false positives in the final detection maps, and (ii) algorithms processing images in a more local manner perform better. Aims. This work aims to improve the SODINN detection performance by introducing new local processing approaches and adapting its learning process accordingly. methods. We propose NA-SODINN, a new deep learning binary classifier based on a convolutional neural network (CNN) that better captures image noise correlations in ADI-processed frames by identifying noise regimes. The identification of these noise regimes is based on a novel technique, named PCA-pmaps, which allowed us to estimate the distance from the star in the image from which background noise started to dominate over residual speckle noise. NA-SODINN was also fed with local discriminators, such as signal-to-noise ratio (S/N) curves, which complement spatio-temporal feature maps during the model's training. Results. Our new approach was tested against its predecessor, as well as two SODINN-based hybrid models and a more standard annular-PCA approach, through local receiving operating characteristics (ROC) analysis of ADI sequences from the VLT/SPHERE and Keck/NIRC-2 instruments. Results show that NA-SODINN enhances SODINN in both sensitivity and specificity, especially in the speckle-dominated noise regime. NA-SODINN is also benchmarked against the complete set of submitted detection algorithms in EIDC, in which we show that its final detection score matches or outperforms the most powerful detection algorithms. Conclusions. Throughout the supervised machine learning case, this study illustrates and reinforces the importanc

关键词： techniques: image processing methods: data analysis methods: statistical planets and satellites: detection techniques: high angular resolution

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Radio-astronomical image reconstruction with a conditional denoising diffusion model

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ASTRONOMY & ASTROPHYSICS 2024年 683卷

作者： Drozdova, M. Kinakh, V. Bait, O. Taran, O. Lastufka, E. Dessauges-Zavadsky, M. Holotyak, T. Schaerer, D. Voloshynovskiy, S. Univ Geneva Dept Comp Sci 7 Route Drize CH-1227 Carouge Switzerland Univ Geneva Observ Geneva 51 Chemin Pegasi CH-1290 Versoix Switzerland

Context. Reconstructing sky models from dirty radio images for accurate source extraction, including source localization and flux estimation, is a complex yet critical task, and has important applications in galaxy evolution studies at high redshift, particularly in deep extragalactic fields using for example the Atacama Large Millimetre Array (ALMA). With the development of large-scale projects, such as the Square Kilometre Array (SKA), we anticipate the need for more advanced source-extraction methods. Existing techniques, such as CLEAN and PyBDSF, currently struggle to effectively extract faint sources, highlighting the necessity for the development of more precise and robust methods. Aims. The success of the source-extraction process critically depends on the quality and accuracy of image reconstruction. As the imaging process represents an "information-lossy" operator, the reconstruction is characterized by uncertainty. The current study proposes the application of stochastic neural networks for the direct reconstruction of sky models from "dirty" images. This approach allows us to localize radio sources and to determine their fluxes with corresponding uncertainties, providing a potential advancement in the field of radio-source characterization. methods. We used a dataset of 10 164 images simulated with the CASA tool based on the ALMA Cycle 5.3 antenna configuration. We applied conditional denoising diffusion probabilistic models (DDPMs) to directly reconstruct sky models from these dirty images, and then processed these models using Photutils to extract the coordinates and fluxes of the sources. To test the robustness of the proposed model, which was trained on a fixed water vapor value, we examined its performance under varying levels of water vapor. Results. We demonstrate that the proposed approach is state of the art in terms of source localisation, achieving over 90% completeness at a signal-to-noise ratio (S/N) of as low as 2. Additionally, the describe

关键词： methods: data analysis techniques: image processing radio continuum: general submillimeter: general

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BP-DIP: A backprojection based deep image prior 28

BP-DIP: A backprojection based deep image prior

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28th European signal processing Conference, EUSIPCO 2020

作者： Zukerman, Jenny Tirer, Tom Giryes, Raja Department of Biomedical Engineering Tel Aviv University Tel Aviv Israel School of Electrical Engineering Tel Aviv University Tel Aviv Israel

ISBN: (纸本)9789082797053

Deep neural networks are a very powerful tool for many computer vision tasks, including image restoration, exhibiting state-of-the-art results. However, the performance of deep learning methods tends to drop once the observation model used in training mismatches the one in test time. In addition, most deep learning methods require vast amounts of training data, which are not accessible in many applications. To mitigate these disadvantages, we propose to combine two image restoration approaches: (i) Deep image Prior (DIP), which trains a convolutional neural network (CNN) from scratch in test time using the given degraded image. It does not require any training data and builds on the implicit prior imposed by the CNN architecture;and (ii) a backprojection (BP) fidelity term, which is an alternative to the standard least squares loss that is usually used in previous DIP works. We demonstrate the performance of the proposed method, termed BP-DIP, on the deblurring task and show its advantages over the plain DIP, with both higher PSNR values and better inference run-time. © 2021 European signal processing Conference, EUSIPCO. All rights reserved.

关键词： image reconstruction

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Digital Halftone Classification using Simplified CNN and stochastic Statistics

Digital Halftone Classification using Simplified CNN and Sto...

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Asia-Pacific-signal-and-Information-processing-Association Annual Summit and Conference (APSIPA ASC)

作者： Guo, Jing-Ming Sankarasrinivasan, S. Natl Taiwan Univ Sci & Technol Taipei Taiwan

ISBN: (纸本)9789881476890

Digital halftone technique is widely utilized in various printing applications to generate high quality printing output. The halftone classification is a fundamental requirement for the optimal reconstruction of binary printed images during inverse halftoning and image authentication. In this study, a new classification scheme based on convolutional neural networks (CCN) is proposed. For effective training, a new approach exploiting the properties of stochastic halftone parameters is proposed to obtain the favorable halftone patch. The CNN network has been optimized to perform classification with minimal layers, and the model is trained using cross entropy, and make use of softmax classification layer. During testing, the sub-halftone patches are classified individually and then majority voting strategy is performed to identify the actual halftone class. For performance evaluation, a deep learning halftone database has been developed for the 24 halftone varieties which also includes the latest multitone images. From experimental result, it has been validated that the proposed architecture achieves average correct classification rate (ACCR) of 100% for several halftone class, and showed superior performance in comparison with state-of-the-art methods.

关键词： CNN classifier digital half-toning stochastic statistics printing

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Comparative Study of Transformer Robustness for Multiple Particle Tracking Without Clutter

Comparative Study of Transformer Robustness for Multiple Par...

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European signal processing Conference (EUSIPCO)

作者： Piyush Mishra Philippe Roudot Aix Marseille Univ CNRS Centrale Med I2M Institut Fresnel Turing Centre for Living Systems Marseille France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

The tracking of multiple particles in image se-quences is challenged by the stochastic nature of displacements, particles detection errors, and the combinatorial explosion of all possible trajectories. As such, extensive work has focused on the modeling of noisy trajectories to try and predict the most likely trajectory-to-measurements associations. Recently, transformers have been shown to significantly accelerate the evaluation of probabilistic models for the system dynamics and detection clutter generated from false positives. However, little work has focused on clutter-free scenarios with multiple particles moving erratically, where the challenge resides not in the model complexity, but in the combinatorial burden of considering all possible trajectory-to-measurements associations. This is a common occurrence in fluorescence microscopy at low framerate. This paper offers a proof-of-concept study of the benefit of the transformer architecture in such scenarios through the simulation of two-particle-systems undergoing Brownian motion. Specifically, we designed a transformer for this estimation task and compared it with the Multiple Hypothesis Tracker (MHT), the optimal estimator when all trajectory-to-measurement associ-ations can be computed. We first show increased robustness of the transformer against erratic displacements over long sequences, with significantly lower computational complexity than MHT. Then, we show that while the transformer requires very little training to significantly outperform MHT on long sequences, it cannot match the theoretically optimal performances of MHT on short sequences even with extensive training. Hence, our work motivates the broader application of transformers in high-SNR sequences and opens the way to the development of frugal methods thanks to the combination of both statistical and neural network frameworks for particle tracking.

关键词： Training Tracking System dynamics stochastic processes signal processing Transformers Robustness Trajectory Particle tracking Clutter

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Are Objective Explanatory Evaluation Metrics Trustworthy? An Adversarial Analysis

Are Objective Explanatory Evaluation Metrics Trustworthy? An...

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IEEE International Conference on image processing

作者： Prithwijit Chowdhury Mohit Prabhushankar Ghassan AlRegib Mohamed Deriche School of Electrical and Computer Engineering OLIVES at the Center for Signal and Information Processing Georgia Institute of Technology Atlanta GA Ajman University United Arab Emirates

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Explainable AI (XAI) has revolutionized the field of deep learning by empowering users to have more trust in neural network models. The field of XAI allows users to probe the inner workings of these algorithms to elucidate their decision-making processes. The rise in popularity of XAI has led to the advent of different strategies to produce explanations, all of which only occasionally agree. Thus several objective evaluation metrics have been devised to decide which of these modules give the best explanation for specific scenarios. The goal of the paper is twofold: (i) we employ the notions of necessity and sufficiency from causal literature to come up with a novel explanatory technique called SHifted Adversaries using Pixel Elimination(SHAPE) which satisfies all the theoretical and mathematical criteria of being a valid explanation, (ii) we show that SHAPE is, infact, an adversarial explanation that fools causal metrics that are employed to measure the robustness and reliability of popular importance based visual XAI methods. Our analysis shows that SHAPE outperforms popular explanatory techniques like GradCAM and GradCAM++ in these tests and is comparable to RISE, raising questions about the sanity of these metrics and the need for human involvement for an overall better evaluation.

关键词： Measurement Visualization Shape Shape measurement neural networks Reliability theory Predictive models

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SHALLOW MULTI-SCALE NETWORK FOR STYLIZED SUPER-RESOLUTION

SHALLOW MULTI-SCALE NETWORK FOR STYLIZED SUPER-RESOLUTION

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IEEE International Conference on image processing (ICIP)

作者： Durand, Thibault Rabin, Julien Tschumperle, David Normandie Univ GREYC CNRS ENSICAENUNICAEN F-14000 Caen France

ISBN: (纸本)9781665441155

image Super Resolution (SR) has come a long way since the early age of image processing. Deep learning methods nowadays give outstanding results, yet very few are actually used in digital illustration and photo retouching software due to large memory storage and GPU computational requirements, but also due to the actual lack of control provided to the user over the final result. This paper introduces a two-step framework for stylized SR using a multi-scale network built with independent parallel branches. The approach aims at: i. designing a shallow network based on image processing techniques making it usable on light hardware architecture (low memory cost, no GPU);ii. providing a versatile, controllable and customizable network to stylize SR results in a plug-and-play manner. We show that the proposed method offers significant advantages over state-of-the-art reference-based approaches regarding these aspects.

关键词： image Super Resolution Style Transfer Shallow neural Network Texture Synthesis Interactive Computation

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