Target Contrast

Target contrast enhancement focuses on improving the visibility and detectability of objects, particularly small or low-contrast targets, within images or videos. Current research emphasizes deep learning approaches, employing architectures like U-Net variations and transformers, often incorporating attention mechanisms and novel loss functions (such as soft dynamic time warping) to address challenges like limited training data and noisy backgrounds. These advancements are crucial for applications ranging from medical imaging (e.g., harmonizing contrast across different MRI scanners) to defense (e.g., improving infrared small target detection), where accurate and reliable object identification is paramount. The ultimate goal is to improve the accuracy and efficiency of object detection and tracking across diverse modalities and challenging conditions.