Core Concepts
Proposing Self-Adaptive Reality-Guided Diffusion (SARGD) for artifact-free super-resolution, enhancing image quality and reducing inference time.
Abstract
The content introduces SARGD as a training-free method for artifact-free super-resolution. It addresses over-smoothing issues and improves image fidelity through Reality-Guided Refinement (RGR) and Self-Adaptive Guidance (SAG). Extensive experiments demonstrate superior results compared to existing methods, reducing sampling steps by 2×. The study includes detailed methodology, experimental setups, comparisons with state-of-the-art methods, ablation studies on denoising strategies, realistic latent update approaches, impact of artifact detection, and inference steps analysis.
Directory:
Abstract
Introduces the concept of artifact-free super-resolution using SARGD.
Introduction
Discusses the challenges in super-resolution techniques.
Methodology
Details the RGR and SAG mechanisms in SARGD.
Experimental Results
Presents results from benchmark datasets and evaluation metrics.
Ablation Study
Analyzes the impact of denoising strategies within RGR.
Influence of Artifact Detection on SARGD
Compares performance with and without artifact detection.
Impact on Inference Steps for SARGD
Illustrates the effect of different time steps on performance.
Stats
Extensive experiments demonstrate superiority of our method, reducing sampling steps by 2×.