Pixel Wise Loss

Pixel-wise loss functions, which assess image quality by comparing individual pixel values between predicted and ground truth images, are a cornerstone of many image processing tasks, including super-resolution, denoising, and segmentation. However, recent research highlights limitations of pixel-wise losses, particularly their tendency to produce blurry outputs or neglect important spatial relationships between pixels, leading to suboptimal results in various applications. Current research focuses on developing alternative or augmented loss functions, such as those incorporating uncertainty, correlation measures (e.g., Pearson correlation), or semantic information, often in conjunction with advanced architectures like transformers and GANs. These efforts aim to improve the accuracy and realism of image processing models, impacting fields ranging from medical imaging to weather forecasting.