Image Hybrid Filtering Research And Implementation Based On Fuzzy Technology

With the development of computer technology, the relationship between computer graphics and the people's daily application has been closer. However, when the image is generated, transmitted and stored, it often exposed to all kinds of noise interference, and this would serious impact on the visual effects. Therefore, many kinds of image processing technologies have been proposed, and the image filtering is one of the key technologies. Image filtering technology must reduce the noise, and at the same time, as much as possible to maintain the image details, and get preparation for the follow-up processing.Through the study of the traditional image filtering algorithm, analyses its characteristics and shortcomings, we have proposed a simple algorithm based on the fuzzy weighted hybrid filtering, which focused on dealing with the uncertainty of the noise, as well as the defects of the traditional algorithm. This algorithm based on fuzzy logic uses the image's characteristics like actual value, mean and median, to get the weighted coefficient, and then calculates the final results through these data.The main work of the following three aspects:1) Proposed an improved membership function. With the analysis of noise characteristics, the improved Gaussian membership function has been proposed, which based on the traditional Gaussian membership function. In theory, the new membership function was closer to the actual noise impact. 2) Proposed an improved defuzzification method. This method uses the noise classification weight as the weight of the defuzzification method, and the output of the membership function only uses as correction of the input.3) Modified the filter inputs. Usually the filter inputs are some absolute image feature values, such as gray values. Consider with using correlation to increase the filtering effect, the filter uses difference of these absolute image feature values, like the difference of the gray value with median and the mean.The series of comparing experiments have shown that this algorithm reduces noise and at the same time as much as possible to maintain the details of the image. Although the algorithm has fully considered the relevance of the pixels and the uncertainty of the noise, but it is simple, does not need the image's statistical information, is a good real-time algorithm.