Zero Shot Image Restoration

Zero-shot image restoration aims to enhance or repair images without requiring any training data specific to the type of degradation present. Current research heavily utilizes pre-trained diffusion models and transformers, often incorporating techniques like attention mechanisms to leverage temporal information in videos or textual guidance for semantically meaningful reconstructions. These methods address various image problems, including super-resolution, deblurring, low-light enhancement, and artifact removal, showing promise for applications ranging from biomedical imaging to enhancing images from challenging environments like under-display cameras on UAVs. The ability to generalize across diverse degradation types without task-specific training is a significant advancement, improving efficiency and applicability in numerous fields.