Image Filtering

Image filtering aims to enhance or modify images by applying various algorithms to alter pixel values, improving image quality or extracting relevant information. Current research focuses on developing novel filter designs, such as adapter-based methods and those leveraging deep learning architectures like convolutional neural networks and transformers, to optimize performance for specific tasks like image classification, segmentation, and video coding. These advancements are improving the efficiency and accuracy of image processing in diverse applications, including medical imaging, surveillance, and autonomous driving, by addressing challenges like noise reduction, edge enhancement, and artifact removal. The development of more efficient and robust filtering techniques continues to be a significant area of investigation.