Studies On Image Processing Methods Based On Fuzzy Information Processing Technique

Image processing is low level operation in image engineering, it plays an important role in image analyzing and computer vision. Because of image processing problem possesses high complexity and the intercross characteristic of subject, the problem has not been good solved for several decades.It occurs information loss when the three dimensions target is projected on the two dimensions image plane; There is fuzzy character in defining boundary, region and texture of image; It brings fuzzy character that explaining the result of the low level image processing. Thus the use of fuzzy information processing technique in image processing is reasonable and inevitability.Based above reasons, a lot of new ideas and methods based fuzzy information processing technique is proposed in this paper, and achieve better results. This dissertation is classified seven chapters, which is organized as follows:In chapter 1, it is illustrated that image processing is basic technique problem in computer vision and intelligent information processing, it is illustrated that the use of fuzzy information processing technique in image processing is reasonable and inevitability. Finally, the main achievements of this dissertation are summarized.In chapter 2, fuzzy mathematics knowledge relating with this dissertation is briefly introduced as mathematics preparation of this dissertation.In chapter 3, a lot of image enhancement local algorithms in space field are proposed. Removing different kinds of impulsive noise in image is systematically researched, and proposes three image removing noise filter based on fuzzy information processing technique, i e., fuzzy entropy-based filter for removing noise in multilevel image, histogram-based filter for removing salt and pepper noise in image, and histogram- based adaptive filter for removing image impulsive noise. Experimental results show that these filters give superior performance compared with conventional filters and other fuzzy filter.In chapter 4, an image enhancement point algorithm in space field is researched. And proposes a new adaptive modifying image grey value method based on fuzzy information processing technique. Through quoting fuzzy entropy, the new method utilizes image neighborhood statistic information, fuzzy information and human visualproperties. The experimentation results show that new algorithm can effectively improve the overall contrast of an image, can increase image visual effect, can sharpen the image edges and avoid much enhancement of noise.In chapter 5, we propose an image enhancement algorithm in transform field based on fuzzy information processing technique. A better performance image fuzzy enhancement arithmetic operator is proposed, the enhancement arithmetic operator is induct into multi-level image fuzzy enhancement algorithm, and get better effect; In addition, through quoting fuzzy entropy, make the select of threshold value in image enhancement have a certainty purpose; Finally, we use our new algorithm to extract image edges, with better result than that of now-available image fuzzy enhancement methods.In chapter 6, through quoting fuzzy entropy, and constructs a lot of better performance information measures. These measures are gotten via input image, and compose a data set that can reflect the natural characters of image edge, and divide the data set into two clusters of the edge data set and not edge data set by weighed FCM. Our method does not need any threshold; more sensitive for weak edge detection.In chapter 7, the damnified image segmentation is deeply researched, the damnified image include three, i e., the noise-corrupted image, the objective and background-overlapped image, the boundary-fuzzy image, and consider that the uncertainty in these image is essentially fuzzy uncertainty, thus, the fuzzy information processing technique is reasonably used in image segmentation, and get better effect.