Impulse noise removal using SVM classification based fuzzy filter from gray scale images.

In this paper, support vector machine (SVM) classification based Fuzzy filter (FF) is proposed for removal of impulse noise from gray scale images. When an image is affected by impulse noise, the quality of the image is distorted since the homogeneity among the pixels is broken. SVM is incorporated for detection of impulse noise from images. Here, a system is trained with an optimal feature set. When an image under test is processed through the trained system, all the pixels under test image will be classified into two classes: noisy and non-noisy. Fuzzy filtering will be performed according to the decision achieved during the testing phase. It provides about 98.5% true-recognition at the time of classification of noisy and non-noisy pixels when image is corrupted by 90% of impulse noise. It leads to improvement of Peak-signal to noise ratio to 22.2437dB for the proposed system when an image is corrupted by 90% of impulse noise. The simulation results also suggest that how this system outperforms some of the state of art methods while preserving structural similarity to a large extent. SVM with histogram based fuzzy filter is proposed to remove impulse noise.Pixels can be either noisy or non-noisy based on knowledge from training phase.Noisy pixels in images are detected even if trained and test image are different.