Reference Guided Image

Reference-guided image processing leverages a second, related image ("reference") to improve the quality or analysis of a target image. Current research focuses on applications like image inpainting (filling missing parts of an image), defect detection, and 3D orientation estimation, employing techniques such as transformer networks, convolutional neural networks, and multi-scale feature alignment to effectively integrate reference information. These methods show promise in enhancing image restoration, improving the accuracy of automated visual inspection, and enabling more robust computer vision tasks, particularly when dealing with incomplete or noisy data. The development of large, publicly available datasets is also contributing to the advancement of this field.