Core Concepts
Entropy-based quantification and contrastive optimization address over-smoothing in image super-resolution models.
Abstract
The article discusses the challenge of over-smoothing in PSNR-oriented image super-resolution models. It introduces the Center-oriented Optimization (COO) problem, where models converge towards the center point of similar high-resolution images rather than the ground truth. The impact of data uncertainty on this problem is quantified using entropy. A novel solution called Detail Enhanced Contrastive Loss (DECLoss) is proposed to reduce variance in potential high-resolution distribution, improving perceptual quality. Experimental results show enhancements in PSNR-oriented and GAN-based models, achieving state-of-the-art performance.
Stats
PSNR: 24.51
LPIPS: 0.093
Downsampled Urban100: 4x
Quotes
"Implicitly optimizing the COO problem, perceptual-driven approaches such as perceptual loss, model structure optimization, or GAN-based methods can be viewed."
"We propose an explicit solution to the COO problem, called Detail Enhanced Contrastive Loss (DECLoss)."
"With the assistance of DECLoss, these methods can surpass a variety of state-of-the-art methods on perceptual metric LPIPS while preserving a high PSNR score."