Image Derivative

Image derivatives, encompassing gradients and higher-order derivatives of image data, are increasingly used to enhance various computer vision and machine learning tasks. Current research focuses on leveraging derivative information to improve model training, particularly for edge detection, regression problems, and neural network optimization, often employing techniques like finite difference approximations and incorporating derivative-based loss functions within neural network architectures. This focus on derivative information leads to more accurate and robust models, impacting fields ranging from image analysis and object detection to the efficient training of implicit neural representations and the solution of partial differential equations.