Image Deblurring and Decomposition: Texture and Color Image Analysis

康0707184 数学图像处理的最新发展之一是Meyer引入的G范数，它为处理纹理和振荡打开了大门。 调查员进一步发展这些技术的彩色图像和模糊纹理分析。 这个项目的目标是探索更复杂的颜色模型的图像分解，以及正确捕捉模糊图像的纹理。主要的挑战是解决图像盲解卷积结合图像分解技术。 调查研究的经典问题imagedebulfilling在现代设置的纹理和彩色imagecalysis。 细节的识别对于更好地诊断医学图像和更准确地恢复卫星图像变得更加重要。 该项目为方法学提供了理论背景，并可应用于解决数字照片处理、医学成像、通信、信息科学和许多其他应用中的实际成像问题。

Kang0707184 One of the recent developments of mathematical imageprocessing is the introduction of the G-norm by Meyer, whichopened the door for handling texture and oscillations. Theinvestigator further develops these techniques for color imagesand blurred texture analysis. The goals of this project are toexplore more sophisticated color models for image decomposition,as well as to correctly capture the texture in blurry images. The main challenge is to tackle image blind deconvolution incombination with image decomposition techniques. The investigator studies the classical problem of imagedeblurring in a modern setting of texture and color imageanalysis. Recognition of detail has become more crucial forbetter diagnosis of medical images and for more accurate recoveryof satellite images. This project provides theoreticalbackground for methodologies and can be applied to solve realimaging problems arising from digital photo restorations, medicalimaging, communications, information science, and many otherapplications.