Image noise filtering using

clangedbivalveAI and Robotics

Oct 19, 2013 (3 years and 7 months ago)

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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 image

S&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 image

Gradients 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 images

How 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 pixels

Filter only noised pixels

A clean pixel is transparent

Noised image

Filtered image

Questions…