Preserve More Details: Mitigating Content Drift in Real-World Image Super-Resolution

• URL: http://arxiv.org/abs/2608.09373v1
• Date: Mon, 10 Aug 2026 09:50:27 GMT
• Title: Preserve More Details: Mitigating Content Drift in Real-World Image Super-Resolution
• Abstract summary: Real-world image super-resolution aims to reconstruct high-quality (HQ) images from low-quality (LQ) inputs subject to diverse real-world degradations.<n>Recent advances have leveraged the LQ inputs and natural image priors learned by Stable Diffusion models to achieve impressive results.<n>We propose FSP-Diff, a novel one-step diffusion model featuring a dual-pathway architecture.
• Score: 6.886139429160757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world image super-resolution (Real-ISR) aims to reconstruct high-quality (HQ) images from low-quality (LQ) inputs subject to diverse real-world degradations. Recent advances have leveraged the LQ inputs and natural image priors learned by Stable Diffusion models to achieve impressive results. However, existing methods often overlook insufficient clarity of LQ inputs inevitably induce content drift in the generated HQ images. This manifests primarily as visual detail degradation and textual semantic shift, severely compromising both fidelity and perceptual quality. To address this challenge, we propose FSP-Diff, a novel one-step diffusion model featuring a dual-pathway architecture. This architecture comprises a Detail-Conditioned Pathway for injecting structured details to recover fine structures, and a Detail-Modulated Semantic Pathway that refines semantic guidance using structured details to mitigate semantic deviations. Extensive experiments on standard Real-ISR benchmarks demonstrate that FSP-Diff surpasses existing one-step diffusion methods in both quantitative and qualitative metrics.

Related papers

• Rarity-Aware Discrete Diffusion with Spatially Consistent Decoding for Photo-Realistic Image Super-Resolution [80.28905986756685]
  DiMOO-SR is a rarity-aware multimodal discrete diffusion framework for photo-realistic image Super-Resolution.<n>Experiments on widely used real-world SR benchmarks demonstrate that DiMOO-SR achieves competitive perceptual quality with only a few parallel decoding steps.
  arXiv  Detail & Related papers  (2026-07-20T07:01:01Z)
• Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution [69.17533212524447]
  We propose a Difficulty-aware Dynamic Routing ( DDR) strategy that overcomes the rigid, one-size-fits-all processing paradigm.<n>Specifically, we design a difficulty estimator to predict the restoration cost of each input image, enabling automatic assignment to a network of appropriate capacity.<n>Extensive experiments have demonstrated the superior efficiency and effectiveness of the proposed model compared to recent state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-07-17T07:49:05Z)
• Zero-shot Adaptation of Stable Diffusion via Plug-in Hierarchical Degradation Representation for Real-World Super-Resolution [22.82705867627899]
  Real-World Image Super-Resolution aims to recover high-quality images from low-quality inputs degraded by unknown and complex real-world factors.<n>textbfHD-CLIP (textbfHierarchical textbfDegradation CLIP) decomposes a low-quality image into a semantic embedding.<n>textbfCLIP can be seamlessly integrated into various super-resolution frameworks without training.
  arXiv  Detail & Related papers  (2025-12-11T06:45:28Z)
• Unlocking the Potential of Diffusion Priors in Blind Face Restoration [63.419272650578165]
  In this work, we use a unified network FLIPNET that switches between two modes to resolve specific gaps.<n>In Restoration mode, the model gradually integrates BFR-oriented features and face embeddings from LQ images to achieve authentic and faithful face restoration.<n>In Degradation mode, the model synthesizes real-world like degraded images based on the knowledge learned from real-world degradation datasets.
  arXiv  Detail & Related papers  (2025-08-12T01:50:55Z)
• One-Step Diffusion-based Real-World Image Super-Resolution with Visual Perception Distillation [53.24542646616045]
  We propose VPD-SR, a novel visual perception diffusion distillation framework specifically designed for image super-resolution (SR) generation.<n>VPD-SR consists of two components: Explicit Semantic-aware Supervision (ESS) and High-frequency Perception (HFP) loss.<n>The proposed VPD-SR achieves superior performance compared to both previous state-of-the-art methods and the teacher model with just one-step sampling.
  arXiv  Detail & Related papers  (2025-06-03T08:28:13Z)
• GuideSR: Rethinking Guidance for One-Step High-Fidelity Diffusion-Based Super-Resolution [15.563111624900865]
  GuideSR is a novel single-step diffusion-based image super-resolution (SR) model specifically designed to enhance image fidelity.<n>Our approach consistently outperforms existing methods across various reference-based metrics including PSNR, SSIM, LPIPS, DISTS and FID.
  arXiv  Detail & Related papers  (2025-05-01T17:48:25Z)
• Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration [19.87693298262894]
  We propose Diff-Restorer, a universal image restoration method based on the diffusion model. We utilize the pre-trained visual language model to extract visual prompts from degraded images. We also design a Degradation-aware Decoder to perform structural correction and convert the latent code to the pixel domain.
  arXiv  Detail & Related papers  (2024-07-04T05:01:10Z)
• DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolution [19.33582308829547]
  This paper proposes to leverage degradation-aligned language prompt for accurate, fine-grained, and high-fidelity image restoration. The proposed method achieves a new state-of-the-art perceptual quality level.
  arXiv  Detail & Related papers  (2024-06-24T09:30:36Z)
• Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion [60.221404321514086]
  Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the limitations of digital imaging in representing varied exposure levels. This paper presents a Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which introduces a bi-level optimization search scheme for automatic design of both network structures and loss functions.
  arXiv  Detail & Related papers  (2023-09-03T08:07:26Z)
• Enhancing Low-light Light Field Images with A Deep Compensation Unfolding Network [52.77569396659629]
  This paper presents the deep compensation network unfolding (DCUNet) for restoring light field (LF) images captured under low-light conditions. The framework uses the intermediate enhanced result to estimate the illumination map, which is then employed in the unfolding process to produce a new enhanced result. To properly leverage the unique characteristics of LF images, this paper proposes a pseudo-explicit feature interaction module.
  arXiv  Detail & Related papers  (2023-08-10T07:53:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.