Single Reference Image

Single reference image techniques aim to perform complex image processing tasks—like inpainting, super-resolution, or stain normalization—using only a single example image as a guide, rather than relying on multiple references. Current research focuses on developing robust algorithms, often based on diffusion models or deep learning architectures incorporating attention mechanisms and novel regularization strategies, to overcome the inherent challenges of limited information. These methods are improving the accuracy and efficiency of various image processing tasks, impacting fields such as medical imaging, microscopy, and computer vision applications requiring high-quality image generation from limited data.