Based Image

Based image processing encompasses a range of techniques using deep learning to manipulate and enhance images, addressing tasks like inpainting (filling missing regions), stitching (combining multiple images), and deraining (removing rain streaks). Current research emphasizes developing efficient and robust models, often employing convolutional neural networks (CNNs), generative adversarial networks (GANs), diffusion models, and wavelet-like transforms, with a focus on improving accuracy, reducing computational cost, and enhancing interpretability. These advancements have significant implications for various applications, including medical imaging (e.g., cleft lip repair simulation), image compression, and improving the robustness of computer vision systems against adversarial attacks.