  PSNR-oriented models are a critical class of super-resolution models with
applications across various fields. However, these models tend to generate
over-smoothed images, a problem that has been analyzed previously from the
perspectives of models or loss functions, but without taking into account the
impact of data properties. In this paper, we present a novel phenomenon that we
term the center-oriented optimization (COO) problem, where a model's output
converges towards the center point of similar high-resolution images, rather
than towards the ground truth. We demonstrate that the strength of this problem
is related to the uncertainty of data, which we quantify using entropy. We
prove that as the entropy of high-resolution images increases, their center
point will move further away from the clean image distribution, and the model
will generate over-smoothed images. 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).
DECLoss utilizes the clustering property of contrastive learning to directly
reduce the variance of the potential high-resolution distribution and thereby
decrease the entropy. We evaluate DECLoss on multiple super-resolution
benchmarks and demonstrate that it improves the perceptual quality of
PSNR-oriented models. Moreover, when applied to GAN-based methods, such as
RaGAN, DECLoss helps to achieve state-of-the-art performance, such as 0.093
LPIPS with 24.51 PSNR on 4x downsampled Urban100, validating the effectiveness
and generalization of our approach.
