Study Of Image Processing Algorithm Based On Fuzzy Logic

The basic concepts, basic theorys and basic methods of digital image processing are summarily introduced firstly. Then the basic concepts and basic theorys of fuzzy logic are introduced. At last, new ideas and methods based on fuzzy logic processing technique are proposed. The research results mainly include the following six aspects:(1) A new adaptive image de-noising algorithm based on fuzzy logic is proposed by analyzing the deficiencies of median filter when it is used to eliminate impulsive noise. The new algorithm based on grayscale distribution of neighboring pixels in different directions detections noise points, and it uses the improved algorithm of maximum-minimum exclusive median method to estimate the gray level of current noisy pixels. Finaly, introducing the fuzzy logic rules into the new algorithm, the gray level of noisy pixels will be restored more reasonably. Simulation results show that the new algorithm may bring better effect in eliminating impulsive noise in comparison with the improved medium filter methods.(2) In research of image denoising, in order to remove noise effectively and preserve edges and key details, an effective image denoising algorithm based on fuzzy logic PDEs is proposed. This algorithm combines the fuzzy logical with the Perona-Malik method. This algorithm builds a new diffusion coefficient in partial derivative equation with the fuzzy membership between the image gradient and the corresponding smooth regions.By defining reasonable fuzzy membership function, the algorithm bases on a selective and improved diffusion coefficient and performes adaptively towards different gradients. Simulated experiments show the algorithm can effectively reduce the noises of the image, and its results needn’t to be adjusted, which can enhancement the precision of edge orientation.(3) An effective PSO fuzzy clustering edge detection algorithm is proposed. PSO (particle swarm optimization) algorithm and Fuzzy C-Mean (FCM) algorithm are combined to overcome two shortcomings, namely the initialization sensitivity and the local minimum of standard FCM algorithm in image edge detection. At first, a vector is constructed to describe edge point informations, which includes neighborhood homogeneity information measure, orientation information measure, and gradient strength. Then we regard a pixel point in a gray image as a data sample, and its gray values which are worked by our defined vector’s operator as the feature vectors of this data sample, in this way we can obtain a data set with three-dimensional features. Then we use the PSO fuzzy clustering algorithm on this data set, it can detect out the edge points adaptively. Simulated experiments show the algorithm can effectively reduce the noises of the image, and its results needn’t to be adjusted, which can enhance the precision of edge orientation.(4) A novel image edge extraction algorithm based on fuzzy enhancement is proposed by analyzing the deficiencies of Pal fuzzy edge extraction algorithm (Pal algorithm). This algorithm introduces fuzzy entropy and selects the threshold value in different gray levels. Defining a new membership function and a new fuzzy enhancement function, the new algorithm can enhance image edges of different gray levels. In addition, the new algorithm simplifies the complex transformation calculation. We can get a better result than that of the traditional Pal image edge extraction algorithm.(5) To determine the optimal thresholds in image segmentation, an effective image threshold segmentation method is presented that base on Fuzzy logic. A new fuzzy entropy is defined, that is not only related to the membership (fuzzy domain) but also related to the probability distribution (space domain), it can respond to the variety of image input information. In addition, by introducing a novel particle swarm optimization (PSO) algorithm, the optimal threshold can be gotten to find the optimization parameters of the membership, so that one image can be segmented by using the threshold. Using our novel algorithm to segment images, we can get a better result than that of most threshold segmentation algorithm.(6) A novel thresholding algorithm is presented. At first, a definition of fuzzy connectedness is proposed. Then the algorithm uses image cut measure as the thresholding principle to distinguish an object from background, the weight matrices are used in evaluating the image cuts measure based on the gray levels of an image, rather than image pixels, for most images, the complexity of the algorithm can be reduced and the speed of the calculation can be improved. Simulation results show that the new algorithm may bring better segmentation effect in comparison with lots of other image thresholding method.