Image noise filtering using artificial neural network Final project by Arie Ohana.

Slides:

Advertisements

Similar presentations

Artificial Neural Networks

Advertisements

1 Machine Learning: Lecture 4 Artificial Neural Networks (Based on Chapter 4 of Mitchell T.., Machine Learning, 1997)

Artificial Neural Networks (1)

Back-propagation Chih-yun Lin 5/16/2015. Agenda Perceptron vs. back-propagation network Network structure Learning rule Why a hidden layer? An example:

Kostas Kontogiannis E&CE

Machine Learning: Connectionist McCulloch-Pitts Neuron Perceptrons Multilayer Networks Support Vector Machines Feedback Networks Hopfield Networks.

Brian Merrick CS498 Seminar.  Introduction to Neural Networks  Types of Neural Networks  Neural Networks with Pattern Recognition  Applications.

Artificial Intelligence (CS 461D)

Simple Neural Nets For Pattern Classification

Radial Basis Functions

November 19, 2009Introduction to Cognitive Science Lecture 20: Artificial Neural Networks I 1 Artificial Neural Network (ANN) Paradigms Overview: The Backpropagation.

1 Chapter 11 Neural Networks. 2 Chapter 11 Contents (1) l Biological Neurons l Artificial Neurons l Perceptrons l Multilayer Neural Networks l Backpropagation.

Neural Networks Marco Loog.

Neural Networks Dr. Peter Phillips. Neural Networks What are Neural Networks Where can neural networks be used Examples Recognition systems (Voice, Signature,

Machine Learning Motivation for machine learning How to set up a problem How to design a learner Introduce one class of learners (ANN) –Perceptrons –Feed-forward.

Presenting: Itai Avron Supervisor: Chen Koren Characterization Presentation Spring 2005 Implementation of Artificial Intelligence System on FPGA.

November 30, 2010Neural Networks Lecture 20: Interpolative Associative Memory 1 Associative Networks Associative networks are able to store a set of patterns.

Data Mining with Neural Networks (HK: Chapter 7.5)

Using Neural Networks to Improve the Performance of an Autonomous Vehicle By Jon Cory and Matt Edwards.

November 21, 2012Introduction to Artificial Intelligence Lecture 16: Neural Network Paradigms III 1 Learning in the BPN Gradients of two-dimensional functions:

Neural Networks. Background - Neural Networks can be : Biological - Biological models Artificial - Artificial models - Desire to produce artificial systems.

Traffic Sign Recognition Using Artificial Neural Network Radi Bekker

Soft Computing Colloquium 2 Selection of neural network, Hybrid neural networks.

MSE 2400 EaLiCaRA Spring 2015 Dr. Tom Way

Presentation on Neural Networks.. Basics Of Neural Networks Neural networks refers to a connectionist model that simulates the biophysical information.

Artificial Neural Networks (ANN). Output Y is 1 if at least two of the three inputs are equal to 1.

Artificial Neural Networks

Explorations in Neural Networks Tianhui Cai Period 3.

1 Chapter 6: Artificial Neural Networks Part 2 of 3 (Sections 6.4 – 6.6) Asst. Prof. Dr. Sukanya Pongsuparb Dr. Srisupa Palakvangsa Na Ayudhya Dr. Benjarath.

Outline What Neural Networks are and why they are desirable Historical background Applications Strengths neural networks and advantages Status N.N and.

Machine Learning Dr. Shazzad Hosain Department of EECS North South Universtiy

Artificial Neural Networks. The Brain How do brains work? How do human brains differ from that of other animals? Can we base models of artificial intelligence.

CAPTCHA solving Tianhui Cai Period 3. CAPTCHAs Completely Automated Public Turing tests to tell Computers and Humans Apart Determines whether a user is.

1 Chapter 11 Neural Networks. 2 Chapter 11 Contents (1) l Biological Neurons l Artificial Neurons l Perceptrons l Multilayer Neural Networks l Backpropagation.

Modelling Language Evolution Lecture 1: Introduction to Learning Simon Kirby University of Edinburgh Language Evolution & Computation Research Unit.

 Based on observed functioning of human brain.  (Artificial Neural Networks (ANN)  Our view of neural networks is very simplistic.  We view a neural.

Over-Trained Network Node Removal and Neurotransmitter-Inspired Artificial Neural Networks By: Kyle Wray.

Introduction to Neural Networks Introduction to Neural Networks Applied to OCR and Speech Recognition An actual neuron A crude model of a neuron Computational.

Face Image-Based Gender Recognition Using Complex-Valued Neural Network Instructor :Dr. Dong-Chul Kim Indrani Gorripati.

Neural Networks Teacher: Elena Marchiori R4.47 Assistant: Kees Jong S2.22

Dr.Abeer Mahmoud ARTIFICIAL INTELLIGENCE (CS 461D) Dr. Abeer Mahmoud Computer science Department Princess Nora University Faculty of Computer & Information.

Face Detection Using Neural Network By Kamaljeet Verma ( ) Akshay Ukey ( )

NEURAL NETWORKS LECTURE 1 dr Zoran Ševarac FON, 2015.

Introduction to Neural Networks Freek Stulp. 2 Overview Biological Background Artificial Neuron Classes of Neural Networks 1. Perceptrons 2. Multi-Layered.

Artificial Neural Networks (ANN). Artificial Neural Networks First proposed in 1940s as an attempt to simulate the human brain’s cognitive learning processes.

Artificial Intelligence CIS 342 The College of Saint Rose David Goldschmidt, Ph.D.

Artificial Intelligence Methods Neural Networks Lecture 3 Rakesh K. Bissoondeeal Rakesh K. Bissoondeeal.

NA-MIC National Alliance for Medical Image Computing BRAINSCut General Tutorial Eun Young(Regina) Kim University of Iowa

Bab 5 Classification: Alternative Techniques Part 4 Artificial Neural Networks Based Classifer.

1 Azhari, Dr Computer Science UGM. Human brain is a densely interconnected network of approximately neurons, each connected to, on average, 10 4.

Machine Learning Artificial Neural Networks MPλ ∀ Stergiou Theodoros 1.

Artificial Neural Networks By: Steve Kidos. Outline Artificial Neural Networks: An Introduction Frank Rosenblatt’s Perceptron Multi-layer Perceptron Dot.

Speech Recognition through Neural Networks By Mohammad Usman Afzal Mohammad Waseem.

INTRODUCTION TO NEURAL NETWORKS 2 A new sort of computer What are (everyday) computer systems good at... and not so good at? Good at..Not so good at..

Fundamental ARTIFICIAL NEURAL NETWORK Session 1st

Artificial Intelligence (CS 370D)

Neural Networks Dr. Peter Phillips.

What is an ANN ? The inventor of the first neuro computer, Dr. Robert defines a neural network as,A human brain like system consisting of a large number.

Artificial Neural Networks for Pattern Recognition

of the Artificial Neural Networks.

XOR problem Input 2 Input 1

Face Recognition with Neural Networks

network of simple neuron-like computing elements

Basics of Deep Learning No Math Required

Creating Data Representations

Neural Networks Geoff Hulten.

Lecture Notes for Chapter 4 Artificial Neural Networks

David Kauchak CS158 – Spring 2019

Outline Announcement Neural networks Perceptrons - continued

Presentation transcript:

Image noise filtering using artificial neural network Final project by Arie Ohana

Image noise High frequency random perturbation in pixels In audio, noise can be a background hiss Total elimination of noise can rarely be found Can use blurring for reduction Many kinds: Additive, Salt & pepper, etc…

Salt & pepper noise A clean imageS&P noise, Density = 0.1

Artificial Neural Network A computing paradigm that is loosely modeled after cortical structures of the brain. Consists of interconnected processing elements called neurons. Achieves its goal by a learning process. The network will adjust itself, by correcting the current weights on every input, according to a predefined formula. Depends heavily on the expressiveness of exemplars.

Neural Network / Structure Output Values Input Signals (External Stimuli) A neuron in the brain Basic perceptron Multi layers ANNs

Approach and Method Running exemplars for 50,000 epochs. Using 4 expressive images Using 1 hidden layer, with 50 neurons Input is a given pixel value along with its surrounding 8 neighbors. Output is single grayscale value (the correction).

The Training Set A detailed image Complex gradients A dichotomy imageGradients and details

Filtering images / Results Complex images, comparing to existing methods

Filtering images / Results Complex images, comparing to existing methods

Filtering images / Results Complex images, comparing to existing methods

Filtering images / Results Less complex, more dichotomy images Artificial simple imagesHow about filtering noise from (beautiful) faces?

Analysis It seems that the network used blurring and whitening (brightening). When zooming in, we can clearly observe the blurring effect The brighten method can clearly be seen

Analysis The histogram of a typical image. Grayscale histogram of the image as produced by the NN. The damage is pretty large. Filtering a complex image

Analysis Filtering a simple image The histogram of a dichotomy image. The histogram the NN produced which very similar to the source.

Conclusions The network used mostly blurring and brightening When comparing to existing methods, they seem preferable Bear in mind: test cases were mostly very complex and difficult Filtering simple dichotomy images was easy for the network

Future work / Improvements Problem: noise is being filtered even in pixels that weren't noised. Image is heavily corrupted, even with existing methods for noise reduction. Solution: build an ANN for recognizing noise only (should be easy and with small False alarm). Use an ANN or other method for filtering noise locally only.

Future work / Improvements Noise / No Noise Greyscale values Output Values Input Signals (External Stimuli) Find noised pixelsFilter only noised pixels A clean pixel is transparent Noised imageFiltered image

Questions…