Image Enhancement And Denosing Based On Fractional Calculus

In this paper,we mainly study image enhancement and image processing based on fractional calculus.For image enhancement,we constructed a new fractional differential mask by discretized the conformable fractional derivatives and proposed an new adaptive fractional differential mask algorithm for image enhancement.As for the image denoising,we proposed a new adaptive fractional image denoising algorithm based on conformable fractional integral.And give a comparison of processed image quality by several image processing effect parameters.According to the content,the full text is divided into five chapters:In chapter one,we introduced the summary of the source of this subject,the development of research,main work and innovation of the full text.The chapter two,we introduced the basic theory of fractional calculus and give several popular definitions of fractional calculus and conformable fractional calculus.Finally,we discussed the advantages of fractional calculus over integer operator in signal processing applications.In chapter three,we studied the image enhancement based on fractional calculus.According to the definition of conformable fractional derivative,we improved the traditional fractional differential mask and propose an adaptive image enhancement method based on conformable fractional derivative.The improved algorithm improved the image enhancement performance of fractional differential mask.The constructed fractional differential mask is applied to image enhancement,and compared with several common integer operator processed images and existing fractional differential mask method processed images.In chapter four,we researched the image denoising based on fractional calculus.First,we introduced the types of image noise and the traditional denoising methods,then we constructed a fractional integral mask based on conformable fractional integral,and obtain a new adaptive fractional image denoising algorithm.Finally,we compared the proposed method with several common image denoising methods.The fifth chapter is the summary and prospect of this paper.