Image Deblurring and Decomposition: Texture and Color Image Analysis

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。研究者进一步发展这些技术用于彩色图像和模糊纹理分析。该项目的目标是探索更复杂的图像分解颜色模型，以及在模糊图像中正确捕获纹理。主要的挑战是如何将图像盲反卷积与图像分解技术相结合。研究者在纹理和彩色图像分析的现代设置中研究图像去模糊的经典问题。细节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至关重要。该项目为方法提供理论背景，可用于解决数字照片修复，医学成像，通信，信息科学和许多其他应用中出现的实际成像问题。

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.