ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction

TL;DR

This paper introduces a novel reinforcement learning-guided fine-tuning approach for latent diffusion models to improve remote sensing image super-resolution, especially in complex scenes, achieving significant quality enhancements.

Contribution

It presents a reinforcement learning-based fine-tuning method for latent diffusion models, enhancing super-resolution performance in remote sensing images with complex scenes.

Findings

PSNR increased by 3-4dB

SSIM improved by 0.08-0.11

LPIPS decreased by 0.06-0.10

Abstract

With the rapid advancement of remote sensing technology, super-resolution image reconstruction is of great research and practical significance. Existing deep learning methods have made progress but still face limitations in handling complex scenes and preserving image details. This paper proposes a reinforcement learning-based latent diffusion model (LDM) fine-tuning method for remote sensing image super-resolution. The method constructs a reinforcement learning environment with states, actions, and rewards, optimizing decision objectives through proximal policy optimization (PPO) during the reverse denoising process of the LDM model. Experiments on the RESISC45 dataset show significant improvements over the baseline model in PSNR, SSIM, and LPIPS, with PSNR increasing by 3-4dB, SSIM improving by 0.08-0.11, and LPIPS reducing by 0.06-0.10, particularly in structured and complex natural scenes. The results demonstrate the method's effectiveness in enhancing super-resolution quality and adaptability across scenes.