Rasterized Image

Rasterized images, essentially bitmap representations of images as grids of pixels, are a fundamental data structure in computer graphics and image processing. Current research focuses on improving efficiency in processing these images, particularly addressing challenges like overlapping elements (e.g., in scatter plots) and the computational cost of processing compressed formats (like JPEGs). Researchers are exploring novel deep learning architectures, including modified U-Net and CNN variations, and optimization techniques to enhance accuracy and speed in tasks such as image segmentation, super-resolution, and automated feature extraction from rasterized data. These advancements have significant implications for diverse fields, including data mining, medical imaging analysis, and efficient rendering in computer graphics.