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

2. 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…

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

4. 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.

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

6. 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).

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

8. Filtering images / Results Complex images, comparing to existing methods

9. Filtering images / Results Complex images, comparing to existing methods

10. Filtering images / Results Complex images, comparing to existing methods

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

12. 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

13. 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

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

15. 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

16. 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.

17. 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

18. Questions…