Research And Application Of The Method Of Inverse Halftoning Based On Super-pixel Segmentation

With the rapid development of computer and network technology,a large number of printed textsare converted into electronic documents spread on t he Internet.As one of th e key technologies of dig ital printing and modern printing,digital halftoning is widely used.Halftoning is a technique that uses a binary image to represent a continuous tone image.It uses the size or density of different points to replace the continuous change of tone.When the scanned image is converted to electronic text,the magnification of the scanner will produce halftone textures or mesh patterns,also known as moire.These textures will degrade the quality of the scanned image and must be processed using imagedescreening algorithm.However,the existing inverse halftone algorithm will cause the image to be blurred after removing the reticulation,the edge of the image and the detailed information have more loss.It is dif ficult to achieve the desired effect.Compared with the pixel,super-pixel can extract the local structure of the image and accurately describe the edge of image,while reducing the complexity of the algorithm.In this paper,we propose comicimage descreening algorithm based on super-pixel segmentation.Firstly,the SLIC super-pixel algorithm is used to segment the comic image and vectored the extracted boundary.Then,the image is subjected to inverse halftone processing to obtain a smooth background image.The vector boundary is int egrated with the smooth background to get the fi nal comic image.The algorithm not o nly can effectively remove the textured image,but also can preserve the edge of the comic image more completely.Medical image processing technology is a combination of mathematics,computer and medical imaging equipment,which is of great significance to promote the development of medical science.However,there are still many problems to be solved.How to maximize the use of medical images to provide useful information,and how to quickly extract the most effective information to analyze the disease,has become the main research content of domestic and foreign scholars.In this paper,the super-pixel segmentation algorithm is applied to the DSA image despeckled in the brain.First,the target vessels enhanced by Frangi filtering are extracted from the background by SLIC super-pixel segmentation.The super-pixel blocks can be well fitted to the edge of the blood vessels.It is difficult to avoid some spots mixed.Therefore,the global threshold algorithm is used to despecklethe segmented super-pixel blocks.The brain blood vessels can be clearly displayed after treatment,help to improve the accuracy of doctors to diagnose the disease.