Image Level

Image-level analysis in computer vision focuses on extracting meaningful information and performing tasks directly from entire images, rather than individual pixels or objects. Current research emphasizes developing robust methods for image quality assessment, including pixel-level quality scores and region-of-interest identification, often employing deep learning architectures like convolutional neural networks and transformers. These advancements are crucial for various applications, such as improving semi-supervised learning, enhancing medical image analysis (e.g., cancer diagnosis, echocardiography), and ensuring the reliability of AI systems in safety-critical domains like autonomous landing.