Perceptual loss functions are designed to capture high-level perceptual differences between images, focusing on content and style discrepancies that may not be evident through traditional pixel-level comparisons. Unlike standard loss functions that measure pixel-wise differences, perceptual loss evaluates the similarity of images based on features extracted from pretrained neural networks, such as VGG. This approach has been effectively applied in tasks like neural style transfer, super-resolution, and image synthesis, where maintaining perceptual quality is crucial.
For a more in-depth understanding, the paper “Perceptual Losses for Real-Time Style Transfer and Super-Resolution” provides valuable insights into the application of perceptual loss functions in enhancing image quality.
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