image noise filtering using artificial neural network final project by arie ohana
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Image noise filtering using Image noise filtering using artificial neural networkartificial neural network
Final project by Arie OhanaFinal project by Arie Ohana
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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…
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Salt & pepper noiseSalt & pepper noise
A clean image S&P noise, Density = 0.1
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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.
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Neural Network / StructureNeural Network / StructureOutput Values
Input Signals (External Stimuli)A neuron in the brain
Basic perceptron Multi layers ANNs
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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).
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The Training SetThe Training Set
A detailed imageComplex gradients
A dichotomy image Gradients and details
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Filtering images / ResultsFiltering images / Results
Complex images, comparing to existing methodsComplex images, comparing to existing methods
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Filtering images / ResultsFiltering images / Results
Complex images, comparing to existing methodsComplex images, comparing to existing methods
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Filtering images / ResultsFiltering images / Results
Complex images, comparing to existing methodsComplex images, comparing to existing methods
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Filtering images / ResultsFiltering images / Results
Less complex, more dichotomy imagesLess complex, more dichotomy images
Artificial simple imagesHow about filtering noise from (beautiful) faces?
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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
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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
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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.
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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
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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.
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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
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QuestionsQuestions……