Image Regularization

Image regularization techniques aim to improve the quality and robustness of image processing by incorporating prior knowledge or constraints into the solution. Current research focuses on developing novel regularization models, including those based on total variation, elastica, and learned potential functions within iterative reweighted least squares frameworks, often implemented as recurrent neural networks. These advancements address challenges in various applications like image deblurring, super-resolution, and medical image analysis, leading to improved accuracy and efficiency in image reconstruction and feature extraction. The resulting improvements have significant implications for diverse fields, enhancing the performance of computer vision systems and facilitating more accurate and reliable analyses in scientific and clinical settings.