Super-resolution reconstruction of single image for latent features
摘要
Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)image.However,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features.This challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model sampling.To address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images.We then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising distribution.Doing so boosts the generative modeling capabilities within a minimal number of sampling steps.Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.
Author：
Xin Wang
Jing-Ke Yan
Jing-Ye Cai
Jian-Hua Deng
Qin Qin
Yao Cheng
作者单位：
School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610000 China
School of Marine Engineering,Guilin University of Electronic Technology,Guilin 541000,China
School of Computer and Information Security,Guilin University of Electronic Technology,Guilin 541000,China
State Key Laboratory of Rail Transit Vehicle System,Southwest Jiaotong University,Chengdu 610000,China
年，卷(期)：
2024, 10(6)
机标分类号：
G202
S
TP311.52
在线出版日期：
2025年4月23日
