CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution

• URL: http://arxiv.org/abs/2503.14272v2
• Date: Wed, 19 Mar 2025 06:41:40 GMT
• Title: CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution
• Authors: Runyi Li, Bin Chen, Jian Zhang, Radu Timofte,
• Abstract summary: Real-world image super-resolution is a critical image processing task, where two key evaluation criteria are the fidelity to the original image and the visual realness of the generated results.<n>We propose a distillation-based approach that leverages the geometric decomposition of both fidelity and realness, alongside the performance advantages of multiple teacher models.<n> Experiments conducted on several real-world image super-resolution benchmarks demonstrate that our method surpasses existing state-of-the-art approaches.
• Score: 52.93785843453579
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world image super-resolution is a critical image processing task, where two key evaluation criteria are the fidelity to the original image and the visual realness of the generated results. Although existing methods based on diffusion models excel in visual realness by leveraging strong priors, they often struggle to achieve an effective balance between fidelity and realness. In our preliminary experiments, we observe that a linear combination of multiple models outperforms individual models, motivating us to harness the strengths of different models for a more effective trade-off. Based on this insight, we propose a distillation-based approach that leverages the geometric decomposition of both fidelity and realness, alongside the performance advantages of multiple teacher models, to strike a more balanced trade-off. Furthermore, we explore the controllability of this trade-off, enabling a flexible and adjustable super-resolution process, which we call CTSR (Controllable Trade-off Super-Resolution). Experiments conducted on several real-world image super-resolution benchmarks demonstrate that our method surpasses existing state-of-the-art approaches, achieving superior performance across both fidelity and realness metrics.

Related papers

• Consistency Trajectory Matching for One-Step Generative Super-Resolution [19.08324232157866]
  Current diffusion-based super-resolution approaches achieve commendable performance at the cost of high inference overhead. We propose Consistency Trajectory Matching for Super-Resolution (CTMSR), a distillation-free strategy that is able to generate photo-realistic SR results in one step. We show that the proposed methods can attain comparable or even superior capabilities on both synthetic and real datasets.
  arXiv  Detail & Related papers  (2025-03-26T09:20:42Z)
• A Simple Combination of Diffusion Models for Better Quality Trade-Offs in Image Denoising [43.44633086975204]
  We propose an intuitive method for leveraging pretrained diffusion models. We then introduce our proposed Linear Combination Diffusion Denoiser. LCDD achieves state-of-the-art performance and offers controlled, well-behaved trade-offs.
  arXiv  Detail & Related papers  (2025-03-18T19:02:19Z)
• OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs [20.652907645817713]
  OFTSR is a flow-based framework for one-step image super-resolution that can produce outputs with tunable levels of fidelity and realism.<n>We demonstrate that OFTSR achieves state-of-the-art performance for one-step image super-resolution, while having the ability to flexibly tune the fidelity-realism trade-off.
  arXiv  Detail & Related papers  (2024-12-12T17:14:58Z)
• Co-learning Single-Step Diffusion Upsampler and Downsampler with Two Discriminators and Distillation [28.174638880324014]
  Super-resolution (SR) aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts.<n>We propose a co-learning framework that jointly optimize a single-step diffusion-based upsampler and a learnable downsampler.
  arXiv  Detail & Related papers  (2024-10-10T07:12:46Z)
• Learning from Multi-Perception Features for Real-Word Image Super-resolution [87.71135803794519]
  We propose a novel SR method called MPF-Net that leverages multiple perceptual features of input images. Our method incorporates a Multi-Perception Feature Extraction (MPFE) module to extract diverse perceptual information. We also introduce a contrastive regularization term (CR) that improves the model's learning capability.
  arXiv  Detail & Related papers  (2023-05-26T07:35:49Z)
• Implicit Diffusion Models for Continuous Super-Resolution [65.45848137914592]
  This paper introduces an Implicit Diffusion Model (IDM) for high-fidelity continuous image super-resolution. IDM integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework. The scaling factor regulates the resolution and accordingly modulates the proportion of the LR information and generated features in the final output.
  arXiv  Detail & Related papers  (2023-03-29T07:02:20Z)
• Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution [29.19388490351459]
  We propose a novel super-resolution model with a low-frequency constraint (LFc-SR) We introduce an ADMM-based alternating optimization method for the non-trivial learning of the constrained model. Experiments showed that our method, without cumbersome post-processing procedures, achieved the state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-08-05T05:37:55Z)
• Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence [76.93002743194974]
  We propose a method to treat arbitrary rescaling, both upscaling and downscaling, as one unified process. The proposed model is able to learn upscaling and downscaling simultaneously and achieve bidirectional arbitrary image rescaling. It is shown to be robust in cycle idempotence test, free of severe degradations in reconstruction accuracy when the downscaling-to-upscaling cycle is applied repetitively.
  arXiv  Detail & Related papers  (2022-03-02T07:42:15Z)
• SIR: Self-supervised Image Rectification via Seeing the Same Scene from Multiple Different Lenses [82.56853587380168]
  We propose a novel self-supervised image rectification (SIR) method based on an important insight that the rectified results of distorted images of the same scene from different lens should be the same. We leverage a differentiable warping module to generate the rectified images and re-distorted images from the distortion parameters. Our method achieves comparable or even better performance than the supervised baseline method and representative state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-11-30T08:23:25Z)
• 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.