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Paper#Image Super-Resolution, Diffusion Models, Computer Vision🔬 ResearchAnalyzed: Jan 3, 2026 09:26

F2IDiff: Super-resolution with Feature-to-Image Diffusion

Published:Dec 30, 2025 21:37
1 min read
ArXiv

Analysis

This paper addresses the limitations of using text-to-image diffusion models for single image super-resolution (SISR) in real-world scenarios, particularly for smartphone photography. It highlights the issue of hallucinations and the need for more precise conditioning features. The core contribution is the introduction of F2IDiff, a model that uses lower-level DINOv2 features for conditioning, aiming to improve SISR performance while minimizing undesirable artifacts.
Reference

“The paper introduces an SISR network built on a FM with lower-level feature conditioning, specifically DINOv2 features, which we call a Feature-to-Image Diffusion (F2IDiff) Foundation Model (FM).”

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Research Paper#Image Super-Resolution, Diffusion Models, Noise Reduction🔬 ResearchAnalyzed: Jan 4, 2026 00:04

Diffusion Posterior Sampling for Super-Resolution with Noise

Published:Dec 25, 2025 22:22
1 min read
ArXiv

Analysis

This paper investigates the application of Diffusion Posterior Sampling (DPS) for single-image super-resolution (SISR) in the presence of Gaussian noise. It's significant because it explores a method to improve image quality by combining an unconditional diffusion prior with gradient-based conditioning to enforce measurement consistency. The study provides insights into the optimal balance between the diffusion prior and measurement gradient strength, offering a way to achieve high-quality reconstructions without retraining the diffusion model for different degradation models.
Reference

“The best configuration was achieved at PS scale 0.95 and noise standard deviation σ=0.01 (score 1.45231), demonstrating the importance of balancing diffusion priors and measurement-gradient strength.”

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