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Deblured Gaussian Blurred Images
JOURNAL OF COMPUTING, VOLUME 2, ISSUE 4, APRIL 2010, ISSN 2151-9617 HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING/ 33 Deblured Gaussian Blurred Images Mr. Salem Saleh Al-amri1, Dr. N.V. Kalyankar2 and Dr. Khamitkar S.D 3 Abstract—This paper attempts to undertake the study of Restored Gaussian Blurred Images. by using four types of techniques of deblurring image as Wiener filter, Regularized filter ,Lucy Richardson deconvlutin algorithm and Blind deconvlution algorithm with an information of the Point Spread Function (PSF) corrupted blurred image with Different values of Size and Alfa and then corrupted by Gaussian noise. The same is applied to the remote sensing image and they are compared with one another, So as to choose the base technique for restored or deblurring image.This paper also attempts to undertake the study of restored Gaussian blurred image with no any information about the Point Spread Function (PSF) by using same four techniques after execute the guess of the PSF, the number of iterations and the weight threshold of it. To choose the base guesses for restored or deblurring image of this techniques. Index Terms—Blur/Types of Blur/PSF; Deblurring/Deblurring Methods. —————————— —————————— 1 INTRODUCTION he restoration image is very important process in the image entire image. Tprocessing to restor the image by using the image processing This type of blurring can be distribution in horizontal and vertical techniques to easily understand this image without any artifacts direction and can be circular averaging by radius -
Sound Masking Done Right: Simple Solutions for Complex Problems
SOUND MASKING DONE RIGHT: SIMPLE SOLUTIONS FOR COMPLEX PROBLEMS Robert Chanaud, Ph.D. Magnum Publishing L.L.C. i Copyright 2008, Robert Chanaud, All Rights Reserved Published with permission by Magnum Publishing, LLC No part of this book may be reproduced, stored in a retrieval system, or transmitted in any other form, or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. While every effort has been made to ensure that the contents of this document are accurate and reliable, Magnum Publishing LLC, Robert Chanaud, or Atlas Sound L.P. cannot assume liability for any damages caused by inaccuracies in the data or documentation, or as a result of the failure of the data, documentation, software, or products described herein to function in a particular manner. The authors and publishers make no warranty, expressed or implied, nor does the fact of distribution constitute a warranty. International Standard Book Number: 978-0-9818166-0-9 Printed in the United States of America ii - Sound Masking Done Right: Simple Solutions for Complex Problems - About Bob Chanaud Dr. Robert C. Chanaud received his BS from the US Coast Guard Academy, his MS from the University of California and his PhD from Purdue University. Active in the field of acoustics since 1958, he taught at Purdue University and the University of Colorado and, in 1975, founded Dynasound, Inc. He has developed software programs to facilitate the design and equalization of masking systems. Acknowledgements The following companies have graciously supplied products for evaluation: Atlas Sound Cambridge Sound Management Dynasound Soft dB Sound Advance Notation Square brackets [ ] are references contained at the end of the manual. -
MARK Serv Grap GLOB 2013 EUROCOM Catalog EN V8 2013
EUROCOM The New Standard for Installed Systems Summer 2013 EUROCOM The EUROCOM Story Innovative Tools Discover EUROCOM, the new for Modern standard for installed systems: Systems Integrators New ideas, breathtaking technologies, a unique design aesthetic, fl exibility – and tremendous value! I’m often asked why BEHRINGER customers love our made them tick. I quickly realized that companies were Over the past two decades we have with a diff erent sales and distribution Longtime BEHRINGER observers will products so much. The answer is simple… it’s because charging US$1000 or more for a piece of equipment, heard from many customers who model, terms of sale, marketing and recognize our EUROLIVE, EURODESK we are so passionate about everything we do. while the components inside were only worth US$100! have used our products in installed support requirements. We recognized and EUROPOWER product family Our customers tell us what they want, and we design sound systems. After all, system early on that the nature of project-driven names. The origin of these names So I immediately grabbed my soldering iron and went to and build products that sound great and provide integrators and contractors are always sales diff ers considerably from that of stems from our roots as a small work on my fi rst signal processor. Word spread quickly amazing feature sets—at extremely aff ordable prices! looking for products that deliver a retail sales and that our organization startup in Germany, hence the among friends that my products sounded really good. would need to adapt in name—EURO. So, of course when It all started 29 years ago when I was studying classical More importantly, I found out that my friends were very order to fully and properly it came time to name the family piano and sound engineering. -
A Comparative Study of Different Noise Filtering Techniques in Digital Images
International Journal of Engineering Research and General Science Volume 3, Issue 5, September-October, 2015 ISSN 2091-2730 A COMPARATIVE STUDY OF DIFFERENT NOISE FILTERING TECHNIQUES IN DIGITAL IMAGES Sanjib Das1, Jonti Saikia2, Soumita Das3 and Nural Goni4 1Deptt. of IT & Mathematics,ICFAI University Nagaland, Dimapur, 797112, India, [email protected] 2Service Engineer, Indian export & import office, Shillong- 793003, Meghalaya, India, [email protected] 3Department of IT, School of Technology, NEHU, Shillong-22, Meghalaya, India, [email protected] 4Department of IT, School of Technology, NEHU, Shillong-22, Meghalaya, India, [email protected] Abstract: Although various solutions are available for de-noising them, a detail study of the research is required in order to design a filter which will fulfil the desire aspects along with handling most of the image filtering issues. In this paper we want to present some of the most commonly used noise filtering techniques namely: Median filter, Gaussian filter, Kuan filter, Morphological filter, Homomorphic Filter, Bilateral Filter and wavelet filter. Median filter is used for reducing the amount of intensity variation from one pixel to another pixel. Gaussian filter is a smoothing filter in the 2D convolution operation that is used to remove noise and blur from image. Kuan filtering technique transforms multiplicative noise model into additive noise model. Morphological filter is defined as increasing idempotent operators and their laws of composition are proved. Homomorphic Filter normalizes the brightness across an image and increases contrast. Bilateral Filter is a non-linear edge preserving and noise reducing smoothing filter for images. Wavelet transform can be applied to image de-noising and it has been extremely successful. -
A Deep Learning Approach to Active Noise Control
A Deep Learning Approach to Active Noise Control Hao Zhang1, DeLiang Wang1;2 1Department of Computer Science and Engineering, The Ohio State University, USA 2Center for Cognitive and Brain Sciences, The Ohio State University, USA fzhang.6720, [email protected] Abstract Reference Error Microphone �(�) Microphone �(�) We formulate active noise control (ANC) as a supervised �(�) �(�) �(�) learning problem and propose a deep learning approach, called �(�) deep ANC, to address the nonlinear ANC problem. A convo- lutional recurrent network (CRN) is trained to estimate the real Cancelling Loudspeaker �(�) and imaginary spectrograms of the canceling signal from the reference signal so that the corresponding anti-noise can elimi- ANC nate or attenuate the primary noise in the ANC system. Large- scale multi-condition training is employed to achieve good gen- Figure 1: Diagram of a single-channel feedforward ANC sys- eralization and robustness against a variety of noises. The deep tem, where P (z) and S(z) denote the frequency responses of ANC method can be trained to achieve active noise cancella- primary path and secondary path, respectively. tion no matter whether the reference signal is noise or noisy speech. In addition, a delay-compensated strategy is intro- tional link artificial neural network to handle the nonlinear ef- duced to address the potential latency problem of ANC sys- fect in ANC. Other nonlinear adaptive models such as radial tems. Experimental results show that the proposed method is basis function networks [12], fuzzy neural networks [13], and effective for wide-band noise reduction and generalizes well to recurrent neural networks [14] have been developed to further untrained noises. -
Sound Masking in Patient Rooms
Navigate to ... Navigate to ... SnoCQ eguraie: nRtuMedPin oait ontaimskgi s highprofile | May 19, 2014 | 0 Comments by Benjamin Davenny Noise levels in hospitals have become an increasing concern as more noise sources have been added to the hospital environment. These sources range from noisy medical instruments to the layout of patient rooms as they relate to the nurses’ station. Numerous studies have evaluated the impact of noise levels on the hospital environment, but few have considered the type of noise source. The sound of a fan is different from an alarm, even if they measure at the same sound levels. Without clear objectives on the type of noises studied, there is a false impression that a quieter environment is always a better one. Some of these noise sources are necessary in a modern working hospital. The trick is to take a different look at their sources and Ben Davenny develop more efficient methods to reduce disturbance to patients. The often-cited World Health Organization (WHO) and Environmental Protection Agency (EPA) guidelines for hospitals require low noise levels in patient rooms, precluding conversation in corridors with patient doors open. This requirement conflicts with nurses’ needs to see patients and discuss patient care. The WHO and EPA guidelines are also based mainly on transportation noise, whose character is quite distinctive and bothersome to building occupants. Introducing a constant noise source as background sound helps to reduce the impact of impulsive tonal noises such as speech. The typical background noise level can be considered a constant noise with full frequency content. These noise sources include air movement from the building’s HVAC system and cooling fans, as well as electronic sound masking systems. -
Active Noise Control in a Three-Dimensional Space Jihe Yang Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1994 Active noise control in a three-dimensional space Jihe Yang Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Acoustics, Dynamics, and Controls Commons, and the Physics Commons Recommended Citation Yang, Jihe, "Active noise control in a three-dimensional space " (1994). Retrospective Theses and Dissertations. 10660. https://lib.dr.iastate.edu/rtd/10660 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. U'M'I MICROFILMED 1994 INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these vrill be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. -
A Simple Second Derivative Based Blur Estimation Technique Thesis
A Simple Second Derivative Based Blur Estimation Technique Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Gourab Ghosh Roy, B.E. Graduate Program in Computer Science & Engineering The Ohio State University 2013 Thesis Committee: Brian Kulis, Advisor Mikhail Belkin Copyright by Gourab Ghosh Roy 2013 Abstract Blur detection is a very important problem in image processing. Different sources can lead to blur in images, and much work has been done to have automated image quality assessment techniques consistent with human rating. In this work a no-reference second derivative based image metric for blur detection and estimation has been proposed. This method works by evaluating the magnitude of the second derivative at the edge points in an image, and calculating the proportion of edge points where the magnitude is greater than a certain threshold. Lower values of this proportion or the metric denote increased levels of blur in the image. Experiments show that this method can successfully differentiate between images with no blur and varying degrees of blur. Comparison with some other state-of-the-art quality assessment techniques on a standard dataset of Gaussian blur images shows that the proposed method gives moderately high performance values in terms of correspondence with human subjective scores. Coupled with the method’s primary aspect of simplicity and subsequent ease of implementation, this makes it a probable choice for mobile applications. ii Acknowledgements This work was motivated by involvement in the personal analytics research group with Dr. -
Machine Learning Based Cyber Attack Targeting on Controlled Information
Machine Learning Based Cyber Attacks Targeting on Controlled Information By Yuantian Miao A thesis submitted in fulfillment for the degree of Doctor of Philosophy Faculty of Science, Engineering and Technology (FSET) Swinburne University of Technology May 2021 Abstract Due to the fast development of machine learning (ML) techniques, cyber attacks utilize ML algorithms to achieve a high success rate and cause a lot of damage. Specifically, the attack against ML models, along with the increasing number of ML-based services, has become one of the most emerging cyber security threats in recent years. We review the ML-based stealing attack in terms of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information. An overall attack methodology is extracted and summarized from the recently published research. When the ML model is the target, the attacker can steal model information or mislead the model’s behaviours. The model information stealing attacks can steal the model’s structure information or model’s training set information. Targeting at Automated Speech Recognition (ASR) system, the membership inference method is studied to whether the model’s training set can be inferred at user-level, especially under the black-box access. Under the label-only black-box access, we analyse user’s statistical information to improve the user-level membership inference results. When even the label is not provided, google search results are collected instead, while fuzzy string matching techniques would be utilized to improve membership inference performance. Other than inferring training set information, understanding the model’s structure information can launch an effective adversarial ML attack. -
Noise Assessment Activities
Noise assessment activities Interesting stories in Europe ETC/ACM Technical Paper 2015/6 April 2016 Gabriela Sousa Santos, Núria Blanes, Peter de Smet, Cristina Guerreiro, Colin Nugent The European Topic Centre on Air Pollution and Climate Change Mitigation (ETC/ACM) is a consortium of European institutes under contract of the European Environment Agency RIVM Aether CHMI CSIC EMISIA INERIS NILU ÖKO-Institut ÖKO-Recherche PBL UAB UBA-V VITO 4Sfera Front page picture: Composite that includes: photo of a street in Berlin redesigned with markings on the asphalt (from SSU, 2014); view of a noise barrier in Alverna (The Netherlands)(from http://www.eea.europa.eu/highlights/cutting-noise-with-quiet-asphalt), a page of the website http://rumeur.bruitparif.fr for informing the public about environmental noise in the region of Paris. Author affiliation: Gabriela Sousa Santos, Cristina Guerreiro, Norwegian Institute for Air Research, NILU, NO Núria Blanes, Universitat Autònoma de Barcelona, UAB, ES Peter de Smet, National Institute for Public Health and the Environment, RIVM, NL Colin Nugent, European Environment Agency, EEA, DK DISCLAIMER This ETC/ACM Technical Paper has not been subjected to European Environment Agency (EEA) member country review. It does not represent the formal views of the EEA. © ETC/ACM, 2016. ETC/ACM Technical Paper 2015/6 European Topic Centre on Air Pollution and Climate Change Mitigation PO Box 1 3720 BA Bilthoven The Netherlands Phone +31 30 2748562 Fax +31 30 2744433 Email [email protected] Website http://acm.eionet.europa.eu/ 2 ETC/ACM Technical Paper 2015/6 Contents 1 Introduction ...................................................................................................... 5 2 Noise Action Plans ......................................................................................... -
Histopathology Learning Dataset Augmentation: a Hybrid Approach
Proceedings of Machine Learning Research { Under Review:1{9, 2021 Full Paper { MIDL 2021 submission HISTOPATHOLOGY LEARNING DATASET AUGMENTATION: A HYBRID APPROACH Ramzi HAMDI1 [email protected] Cl´ement BOUVIER1 [email protected] Thierry DELZESCAUX2 [email protected] C´edricCLOUCHOUX1 [email protected] 1 WITSEE, Neoxia, Paris, France 2 CEA-CNRS-UMR 9199, LMN, MIRCen, Univ. Paris-Saclay, Fontenay-aux-Roses, France Editors: Under Review for MIDL 2021 Abstract In histopathology, staining quantification is a mandatory step to unveil and characterize disease progression and assess drug efficiency in preclinical and clinical settings. Super- vised Machine Learning (SML) algorithms allow the automation of such tasks but rely on large learning datasets, which are not easily available for pre-clinical settings. Such databases can be extended using traditional Data Augmentation methods, although gen- erated images diversity is heavily dependent on hyperparameter tuning. Generative Ad- versarial Networks (GAN) represent a potential efficient way to synthesize images with a parameter-independent range of staining distribution. Unfortunately, generalization of such approaches is jeopardized by the low quantity of publicly available datasets. To leverage this issue, we propose a hybrid approach, mixing traditional data augmentation and GAN to produce partially or completely synthetic learning datasets for segmentation application. The augmented datasets are validated by a two-fold cross-validation analysis using U-Net as a SML method and F-Score as a quantitative criterion. Keywords: Generative Adversarial Networks, Histology, Machine Learning, Whole Slide Imaging 1. Introduction Histology is widely used to detect biological objects with specific staining such as protein deposits, blood vessels or cells. -
Anisotropic Diffusion in Image Processing
Anisotropic diffusion in image processing Tomi Sariola Advisor: Petri Ola September 16, 2019 Abstract Sometimes digital images may suffer from considerable noisiness. Of course, we would like to obtain the original noiseless image. However, this may not be even possible. In this thesis we utilize diffusion equations, particularly anisotropic diffusion, to reduce the noise level of the image. Applying these kinds of methods is a trade-off between retaining information and the noise level. Diffusion equations may reduce the noise level, but they also may blur the edges and thus information is lost. We discuss the mathematics and theoretical results behind the diffusion equations. We start with continuous equations and build towards discrete equations as digital images are fully discrete. The main focus is on iterative method, that is, we diffuse the image step by step. As it occurs, we need certain assumptions for these equations to produce good results, one of which is a timestep restriction and the other is a correct choice of a diffusivity function. We construct an anisotropic diffusion algorithm to denoise images and compare it to other diffusion equations. We discuss the edge-enhancing property, the noise removal properties and the convergence of the anisotropic diffusion. Results on test images show that the anisotropic diffusion is capable of reducing the noise level of the image while retaining the edges of image and as mentioned, anisotropic diffusion may even sharpen the edges of the image. Contents 1 Diffusion filtering 3 1.1 Physical background on diffusion . 3 1.2 Diffusion filtering with heat equation . 4 1.3 Non-linear models .