Signal Processing and Interpretable Deep Network for Image Reconstruction

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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.