Signal Processing and Interpretable Deep Network for Image Reconstruction

用于图像重建的信号处理和可解释深度网络

• 批准号: 2784995
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2784995
• 关键词: Signal; Processing; Interpretable; Deep; Network

Image restoration is an inverse problem related to obtaining a high-quality image from corrupted input measured data. It is relevant to many real-world applications including medical imaging and microscopy. For example, image restoration technology for magnetic resonance imaging (MRI) can effectively reduce the scanning cost by collecting a proportion of data and inferring the whole data. Recently, with the development of artificial intelligence, many learning-based methods were developed for handling image restoration problems. These methods construct image-restoration models by learning from high-quality and low-quality image pairs and achieve competitive results with fast speed. Despite the evident success of the learning-based methods, many of them are designed based on an assumption that the degradation process from high-quality image to low-quality image is known and linear. However, the degradation processes in real-world scenarios are more complicated and unknown in most cases. Therefore, my research aims to break this obstacle by proposing a novel invertible framework to handle image restoration problems. The flexibility of invertible neural network structure has the potential to simulate unknown, non-linear degradation processes in real-world scenarios. Furthermore, the proposed method could be suitable for practical usage by providing a lightweight memory-efficient model because it uses a constant amount of memory to compute gradients, regardless of the depth of the network.

图像恢复是与从损坏的输入测量数据获取高质量图像相关的逆问题。它与许多现实世界的应用相关，包括医学成像和显微镜。例如，磁共振成像（MRI）的图像修复技术可以通过收集一部分数据并推断整体数据来有效降低扫描成本。最近，随着人工智能的发展，开发了许多基于学习的方法来处理图像恢复问题。这些方法通过从高质量和低质量图像对中学习来构建图像恢复模型，并快速获得有竞争力的结果。尽管基于学习的方法取得了明显的成功，但其中许多方法是基于这样的假设而设计的：从高质量图像到低质量图像的退化过程是已知的且是线性的。然而，在大多数情况下，现实场景中的退化过程更加复杂且未知。因此，我的研究旨在通过提出一种新颖的可逆框架来处理图像恢复问题来打破这一障碍。可逆神经网络结构的灵活性具有模拟现实场景中未知的非线性退化过程的潜力。此外，所提出的方法可以通过提供轻量级内存高效模型来适合实际使用，因为无论网络深度如何，它都使用恒定量的内存来计算梯度。