Insights on loss function engineering.
In the realm of deep learning for image enhancement, the design of loss functions is pivotal in guiding models toward generating high-quality outputs. Traditional metrics like Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) have been widely used to measure the difference between predicted and target images. However, these pixel-based losses often lead to overly smoothed results that lack perceptual fidelity, as they tend to average out fine details, resulting in blurred images.