Background Removal

Background removal aims to isolate objects of interest from their surroundings in images and videos, improving image quality and enabling downstream tasks like object recognition and image editing. Current research focuses on developing efficient and robust algorithms, often employing neural networks with architectures like diffusion models, autoencoders, and convolutional neural networks, to achieve high-quality background removal while preserving fine details and maintaining consistency. These advancements have significant implications for various fields, including microscopy, medical imaging, and computer vision applications requiring precise object segmentation and improved image clarity.