DNF-SR: Dual-Input and Negative-Aware Feature Fine-Tuning for Real-World Image Super-Resolution

• URL: http://arxiv.org/abs/2609.15120v1
• Date: Mon, 14 Sep 2026 06:53:22 GMT
• Title: DNF-SR: Dual-Input and Negative-Aware Feature Fine-Tuning for Real-World Image Super-Resolution
• Abstract summary: Unmediatedly feeding LR into a diffusion model creates a distributional gap with the model's original input.<n>We propose a Dual-input and Negative-aware Feature fine-tuning method for Real-ISR.<n>We exploit this property and propose a post-training optimization method, Negative-aware Feature Fine-Tuning (NF2T), which guides the model toward producing higher-quality results.
• Score: 50.847827209157494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Benefiting from the powerful generative priors of diffusion models, diffusion-based real-world image super-resolution (Real-ISR) methods have demonstrated impressive performance.To achieve efficient Real-ISR, several recent works have designed one-step diffusion-based models.Howerver, unmediatedly feeding LR into a diffusion model creates a distributional gap with the model's original input.A straightforward approach to reduce the distribution gap is to introduce noise to the LR latents. However, directly adding noise inevitably corrupts the content of the LR images.In this study, we propose DNF-SR, a Dual-input and Negative-aware Feature fine-tuning method for Real-ISR.Specifically, we use a dual-input strategy that concatenates the original LR image with the noisy LR input and feeds them into a diffusion-based image editing model, ensuring both high-fidelity one-step super-resolution and improved perceptual and content consistency.Additionally, the noise present in the noisy LR input introduces randomness and diversity into the outputs. We exploit this property and propose a post-training optimization method, Negative-aware Feature Fine-Tuning (NF2T), which guides the model toward producing higher-quality results.NF^2T classifies multiple outputs into positive and negative subsets and then defines implicit policy improvement directions in both the image and feature spaces, thereby further enhancing the stability of the optimization.Extensive experiments show that DNF-SR outperforms other methods.Code will be released.

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)
• Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement [32.05719533625606]
  We propose a noise-started one-step Real-ISR framework via LR-conditioned SplitMeanFlow and GAN refinement.<n> SMFSR achieves state-of-the-art quality among one-step diffusion-based Real-ISR methods while retaining fast single-step inference.
  arXiv  Detail & Related papers  (2026-05-10T05:01:32Z)
• LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction [4.609499769793957]
  Diffusion-based image super-resolution (SR) aims to reconstruct high-resolution (HR) images from corresponding low-resolution (LR) observations.<n>The state-of-the-art residual-shifting diffusion framework efficient 4-step inference suffers from severe degradation performance in compact sampling trajectories.<n>We propose a prior-enhanced efficient diffusion framework to address these issues.
  arXiv  Detail & Related papers  (2026-03-22T03:52:38Z)
• LucidNFT: LR-Anchored Multi-Reward Preference Optimization for Generative Real-World Super-Resolution [21.290660354883595]
  Preference-based reinforcement learning (RL) is a natural fit because each LR input yields a rollout group of candidates to compare.<n>We propose LucidNFT, a multi-reward RL framework for flow-matching Real-ISR.<n>Experiments show that LucidNFT consistently improves strong flow-based Real-ISR baselines.
  arXiv  Detail & Related papers  (2026-03-06T06:30:34Z)
• One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation [60.54811860967658]
  FluxSR is a novel one-step diffusion Real-ISR based on flow matching models.<n>First, we introduce Flow Trajectory Distillation (FTD) to distill a multi-step flow matching model into a one-step Real-ISR.<n>Second, to improve image realism and address high-frequency artifact issues in generated images, we propose TV-LPIPS as a perceptual loss.
  arXiv  Detail & Related papers  (2025-02-04T04:11:29Z)
• Latent Diffusion, Implicit Amplification: Efficient Continuous-Scale Super-Resolution for Remote Sensing Images [7.920423405957888]
  E$2$DiffSR achieves superior objective metrics and visual quality compared to the state-of-the-art SR methods. It reduces the inference time of diffusion-based SR methods to a level comparable to that of non-diffusion methods.
  arXiv  Detail & Related papers  (2024-10-30T09:14:13Z)
• ControlSR: Taming Diffusion Models for Consistent Real-World Image Super Resolution [68.72454974431749]
  We present ControlSR, a new method that can tame Diffusion Models for consistent real-world image super-resolution (Real-ISR)<n>Our model can achieve better performance across multiple metrics on several test sets and generate more consistent SR results with LR images than existing methods.
  arXiv  Detail & Related papers  (2024-10-18T08:35:57Z)
• SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models [19.17571465274627]
  Single image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from the given low-resolution (LR) ones. We propose a novel single image super-resolution diffusion probabilistic model (SRDiff) SRDiff is optimized with a variant of the variational bound on the data likelihood and can provide diverse and realistic SR predictions.
  arXiv  Detail & Related papers  (2021-04-30T12:31:25Z)
• Characteristic Regularisation for Super-Resolving Face Images [81.84939112201377]
  Existing facial image super-resolution (SR) methods focus mostly on improving artificially down-sampled low-resolution (LR) imagery. Previous unsupervised domain adaptation (UDA) methods address this issue by training a model using unpaired genuine LR and HR data. This renders the model overstretched with two tasks: consistifying the visual characteristics and enhancing the image resolution. We formulate a method that joins the advantages of conventional SR and UDA models.
  arXiv  Detail & Related papers  (2019-12-30T16:27:24Z)

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.