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Towards Realistic Data Generation for Real-World Super-Resolution

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arxiv 2406.07255 v4 pith:I2CQFDM2 submitted 2024-06-11 cs.CV eess.IV

Long Peng , Wenbo Li , Renjing Pei , Jingjing Ren , Jiaqi Xu , Yang Wang , Yang Cao , Zheng-Jun Zha This is my paper
classification cs.CVeess.IV
keywords datareal-worldrealisticsuper-resolutiondecoupleddegradationsgenerationimages
verification ladder T0 review T1 audit T2 compute T3 formal
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Existing image super-resolution (SR) techniques often fail to generalize effectively in complex real-world settings due to the significant divergence between training data and practical scenarios. To address this challenge, previous efforts have either manually simulated intricate physical-based degradations or utilized learning-based techniques, yet these approaches remain inadequate for producing large-scale, realistic, and diverse data simultaneously. In this paper, we introduce a novel Realistic Decoupled Data Generator (RealDGen), an unsupervised learning data generation framework designed for real-world super-resolution. We meticulously develop content and degradation extraction strategies, which are integrated into a novel content-degradation decoupled diffusion model to create realistic low-resolution images from unpaired real LR and HR images. Extensive experiments demonstrate that RealDGen excels in generating large-scale, high-quality paired data that mirrors real-world degradations, significantly advancing the performance of popular SR models on various real-world benchmarks.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Training

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A transfer training scheme converts Stable Diffusion's 8x VAE into a 4x VAE that stays compatible with the pretrained UNet, improving fine-structure preservation in real-world super-resolution at lower FLOPs.

  2. Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution

    cs.CV 2026-07 reject novelty 4.0 of 10

    DDR-SR routes each real-world low-resolution image to one of two diffusion experts based on a high-frequency-loss difficulty score, using a low-compression VAE for hard images and a high-compression VAE for easy image...

  3. Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignment

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  4. FADE: Adversarial Concept Erasure in Flow Models

    cs.CV 2025-07 reject novelty 4.0 of 10

    FADE combines adversarial training with trajectory preservation to erase concepts from diffusion models, reporting state-of-the-art erasure on Stable Diffusion benchmarks, but the evidence is incomplete and the theore...

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