Image Preprocessing

Image preprocessing enhances the quality and usability of images before further analysis or processing, aiming to improve the performance of downstream tasks like object recognition, compression, and medical image analysis. Current research focuses on developing specialized preprocessing techniques for diverse image types (e.g., infrared, plenoptic, medical) often employing neural networks, including U-Net architectures and transformer-based models, to address challenges such as noise reduction, artifact removal, and feature extraction. These advancements are crucial for improving the accuracy and efficiency of various applications, ranging from autonomous driving and e-commerce to medical diagnostics and efficient data storage. The development of unified preprocessing frameworks that adapt to different downstream tasks and image modalities is also a significant area of ongoing investigation.