Researches And Analysis On Some Image Filter Algorithms

Images are often corrupted by different kinds of noises due to errors generated by noise sensors or communication channels,which makes it more difficult to do some subsequent image processing,such as edge detection,image segmentation and object recognition,so noise detection and removal becomes part and parcel in image processing,and has arrows widely researching interest in recent years. In this article,a fuzzy operator is proposed and verified to evaluate the noise density of impulse noise.Based on this operator,two new filter algorithms are presented for removing impulse noise.Therefore ,better results are expected by experimental comparation.Firstly, the definition of digital images,the image noise model, some traditional filtering algorithms and their evaluating methods are introduced,then the basic mathematical theory used in the following two chapers is given,including the elementary fuzzy mathematics theory and some information about curve fitting.Chapter 3 and chaper 4 are the main fruits of the author,they are the most important parts of the article as well.In chaper 3,at the beginning,the lately impulse noise removal algorithms are summerised;then a fuzzy indicator to evaluate the impulse noise intensity(density) is put forward grounded on fuzzy mathematical theory and the characters of impulse noise,through experimental statistics and curve fitting,the correlation function between noise intensity and fuzzy indicator is obtained and tested;finally,the Priwitt gradient operator is brought in,using restrictive gradient threshold getting from experiment,the median filter algorithm is improved,which leads to better results in saving thin details.As the fuzzy indicator can judge the noise density,the improved median filter is more adaptive.In chapter 4, focus on the highly corrupted (>50%)images by impulse noise,first give a good noise removal algorithm—MMEM algorithm,then a new noise detection algorithm pixel by pixel is proposed using sequential images ,according to the positive and negative character of impulse noise.It searches the useful information in each image sufficiently to restore the corrupted images and reaches better result compared with traditional algorithms.In conclusion,chapter 5 sums up the whole article and gives some new ideas and opinionson some questions in the paper.