Pixel Wise Regression

Pixel-wise regression involves predicting a continuous value for each pixel in an image, enabling tasks like depth estimation, optical flow prediction, and motion blur removal. Current research focuses on improving accuracy and efficiency through novel loss functions, adaptive sampling techniques, and the use of architectures like U-Nets and kernel prediction networks, often incorporating probabilistic methods for uncertainty quantification. These advancements are crucial for enhancing the reliability and robustness of computer vision systems in applications ranging from autonomous driving to remote sensing, particularly in scenarios demanding high precision and uncertainty awareness.