Spatially Varying Regularization

Spatially varying regularization enhances traditional regularization techniques by adapting penalty strengths across different image regions, improving the accuracy and detail preservation of solutions to inverse problems. Current research focuses on integrating this approach with deep learning models, particularly convolutional neural networks and transformers, to learn data-driven, spatially-adaptive regularization parameters. This allows for optimized solutions in various applications like medical imaging reconstruction and image registration, surpassing the limitations of uniform regularization methods and leading to higher-quality results with fewer data requirements. The resulting improvements in image quality and efficiency have significant implications for diverse fields requiring accurate image processing and analysis.