Efficient computational tools for inverse imaging problems

用于逆成像问题的高效计算工具

• 批准号: EP/M00483X/1
• 负责人: Carola-Bibiane Schönlieb
• 金额: $ 67.17万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM00483X%2F1
• 关键词: Efficient; computational; tools; inverse; imaging

A photograph taken with current state-of-the-art digital cameras has between 10 to 20 million pixels. Some cameras have up to 41 million sensor pixels. Despite advances in sensor and optical technology, technically perfect photographs are still elusive in demanding conditions. In low light even the best cameras produce noisy images. Casual photographers cannot always hold the camera steady, and the photograph becomes blurry despite advanced shake reduction technologies. We are thus presented with the challenge of improving the photographs in post-processing. This would desirably be an automated process, based on mathematically well understood models that can be relied upon.The difficulty with real photographs of tens of millions of pixels is that the resulting optimisation problems -- the task of finding the best enhanced image according to a model -- are huge, and computationally very intensive. Moreover, imaging problems generally computationally very intensive. State-of-the-art image processing techniques based on mathematical principles are only up to processing small images in real time. Further, choosing the right parameters for the models can be difficult. Parameter choice can be facilitated, but again in computationally very intensive ways. The question now is, can we design faster optimisation algorithms that would make this and other image processing tasks tractable for real high-resolution photographs?The objective of the proposed project is to develop optimisation algorithms that are up to this task. The focus of the project is on general methods that will be applicable to a wide variety of image processing tasks and general big data problems. Besides photography, we will apply the developed tools to problems from biology and medicine, involving magnetic resonance imaging and microscopy.

使用当前最先进的数码相机拍摄的照片具有 10 至 2000 万像素。有些相机的传感器像素高达 4100 万。尽管传感器和光学技术取得了进步，但在苛刻的条件下，技术上完美的照片仍然难以实现。在弱光下，即使是最好的相机也会产生嘈杂的图像。休闲摄影师无法始终稳定地握住相机，尽管采用了先进的防抖技术，照片还是会变得模糊。因此，我们面临着在后期处理中改进照片的挑战。这最好是一个自动化的过程，基于数学上易于理解的模型。数千万像素的真实照片的困难在于，由此产生的优化问题——根据模型找到最佳增强图像的任务——是巨大的，并且计算量非常大。此外，成像问题通常计算量非常大。基于数学原理的最先进的图像处理技术只能实时处理小图像。此外，为模型选择正确的参数可能很困难。可以促进参数选择，但同样以计算量非常大的方式进行。现在的问题是，我们能否设计出更快的优化算法，使该任务和其他图像处理任务能够处理真正的高分辨率照片？该项目的目标是开发适合该任务的优化算法。该项目的重点是适用于各种图像处理任务和一般大数据问题的通用方法。除了摄影之外，我们还将把开发的工具应用于生物学和医学问题，包括磁共振成像和显微镜。

项目成果

GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays.

• DOI: 10.1016/j.patcog.2021.108274
• 发表时间: 2022-03
• 期刊: Pattern recognition
• 影响因子: 8
• 作者: Aviles-Rivero AI;Sellars P;Schönlieb CB;Papadakis N
• 通讯作者: Papadakis N

Mini-Workshop: Deep Learning and Inverse Problems

迷你研讨会：深度学习与反问题

• DOI: 10.4171/owr/2018/11
• 发表时间: 2019
• 期刊: Oberwolfach Reports
• 作者: Arridge S

Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part III

医学图像计算和计算机辅助干预 - MICCAI 2022 - 第 25 届国际会议，新加坡，2022 年 9 月 18-22 日，会议记录，第三部分

• DOI: 10.1007/978-3-031-16437-8_69
• 发表时间: 2022
• 作者: Aviles-Rivero A

Solving inverse problems using data-driven models

• DOI: 10.1017/s0962492919000059
• 发表时间: 2019-01-01
• 期刊: ACTA NUMERICA
• 影响因子: 14.2
• 作者: Arridge, Simon;Maass, Peter;Schonlieb, Carola-Bibiane
• 通讯作者: Schonlieb, Carola-Bibiane

Task adapted reconstruction for inverse problems

• DOI: 10.1088/1361-6420/ac28ec
• 发表时间: 2022-07-01
• 期刊: INVERSE PROBLEMS
• 影响因子: 2.1
• 作者: Adler, Jonas;Lunz, Sebastian;Oktem, Ozan
• 通讯作者: Oktem, Ozan