Empowering Real-World Image Super-Resolution With Flexible Interactive Modulation.

Interactive image restoration aims to construct an interactive pathway between users and restoration networks, which empowers users to modulate the restoration results according to their own demands. However, existing methods are primarily limited to training their networks with predefined and simplistic synthetic degradations. Consequently, these methods often encounter significant performance degradation when confronted with real-world degradations that deviate from their assumptions.

TransCL: Transformer Makes Strong and Flexible Compressive Learning.

Compressive learning (CL) is an emerging framework that integrates signal acquisition via compressed sensing (CS) and machine learning for inference tasks directly on a small number of measurements. It can be a promising alternative to classical image-domain methods and enjoys great advantages in memory saving and computational efficiency. However, previous attempts on CL are not only limited to a fixed CS ratio, which lacks flexibility, but also limited to MNIST/CIFAR-like datasets and do not scale to complex real-world high-resolution (HR) data or vision tasks.