Image noise filtering using Image noise filtering using artificial neural networkartificial neural network

Final project by Arie OhanaFinal project by Arie Ohana

Image noiseImage noise

High frequency random perturbation in pixels High frequency random perturbation in pixels

In audio, noise can be a background hissIn audio, noise can be a background hiss

Total elimination of noise can rarely be foundTotal elimination of noise can rarely be found

Can use blurring for reductionCan use blurring for reduction

Many kinds: Additive, Salt & pepper, etc…Many kinds: Additive, Salt & pepper, etc…

Salt & pepper noiseSalt & pepper noise

A clean image S&P noise, Density = 0.1

Artificial Neural NetworkArtificial Neural Network

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

Neural Network / StructureNeural Network / StructureOutput Values

Input Signals (External Stimuli)A neuron in the brain

Basic perceptron Multi layers ANNs

Approach and MethodApproach and Method

Running exemplars for 50,000 epochs.Running exemplars for 50,000 epochs.

Using 4 expressive imagesUsing 4 expressive images

Using 1 hidden layer, with 50 neuronsUsing 1 hidden layer, with 50 neurons

Input is a given pixel value along with its Input is a given pixel value along with its surrounding 8 neighbors.surrounding 8 neighbors.

Output is single grayscale value (the Output is single grayscale value (the correction). correction).

The Training SetThe Training Set

A detailed imageComplex gradients

A dichotomy image Gradients and details

Filtering images / ResultsFiltering images / Results

Complex images, comparing to existing methodsComplex images, comparing to existing methods

Filtering images / ResultsFiltering images / Results

Complex images, comparing to existing methodsComplex images, comparing to existing methods

Filtering images / ResultsFiltering images / Results

Complex images, comparing to existing methodsComplex images, comparing to existing methods

Filtering images / ResultsFiltering images / Results

Less complex, more dichotomy imagesLess complex, more dichotomy images

Artificial simple imagesHow about filtering noise from (beautiful) faces?

AnalysisAnalysis

It seems that the network used blurring It seems that the network used blurring and whitening (brightening).and whitening (brightening).

When zooming in, we can clearly observe the blurring effect The brighten method can clearly be seen

AnalysisAnalysis

The histogram of a typical image.

Grayscale histogram of the image as produced by the NN.

The damage is pretty large.

Filtering a complex imageFiltering a complex image

AnalysisAnalysis

Filtering a simple imageFiltering a simple image

The histogram of a dichotomy image.

The histogram the NN produced which very similar to the source.

ConclusionsConclusions

The network used mostly blurring and The network used mostly blurring and brighteningbrightening

When comparing to existing methods, they When comparing to existing methods, they seem preferableseem preferable

Bear in mind: test cases were mostly very Bear in mind: test cases were mostly very complex and difficultcomplex and difficult

Filtering simple dichotomy images was Filtering simple dichotomy images was easy for the networkeasy for the network

Future work / ImprovementsFuture work / Improvements

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

Future work / ImprovementsFuture work / Improvements

Noise / No Noise

Greyscale values

Output Values

Input Signals (External Stimuli)

Find noised pixels Filter only noised pixels

A clean pixel is transparent

Noised image Filtered image

QuestionsQuestions……