Toward Real-World Super-Resolution via Adaptive Downsampling Models

• URL: http://arxiv.org/abs/2109.03444v1
• Date: Wed, 8 Sep 2021 06:00:32 GMT
• Title: Toward Real-World Super-Resolution via Adaptive Downsampling Models
• Authors: Sanghyun Son and Jaeha Kim and Wei-Sheng Lai and Ming-Husan Yang and Kyoung Mu Lee
• Abstract summary: This study proposes a novel method to simulate an unknown downsampling process without imposing restrictive prior knowledge. We propose a generalizable low-frequency loss (LFL) in the adversarial training framework to imitate the distribution of target LR images without using any paired examples.
• Score: 58.38683820192415
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Most image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs that are constructed by a predetermined operation, e.g., bicubic downsampling. As existing methods typically learn an inverse mapping of the specific function, they produce blurry results when applied to real-world images whose exact formulation is different and unknown. Therefore, several methods attempt to synthesize much more diverse LR samples or learn a realistic downsampling model. However, due to restrictive assumptions on the downsampling process, they are still biased and less generalizable. This study proposes a novel method to simulate an unknown downsampling process without imposing restrictive prior knowledge. We propose a generalizable low-frequency loss (LFL) in the adversarial training framework to imitate the distribution of target LR images without using any paired examples. Furthermore, we design an adaptive data loss (ADL) for the downsampler, which can be adaptively learned and updated from the data during the training loops. Extensive experiments validate that our downsampling model can facilitate existing SR methods to perform more accurate reconstructions on various synthetic and real-world examples than the conventional approaches.

Related papers

• Fast constrained sampling in pre-trained diffusion models [77.21486516041391]
  Diffusion models have dominated the field of large, generative image models. We propose an algorithm for fast-constrained sampling in large pre-trained diffusion models.
  arXiv  Detail & Related papers  (2024-10-24T14:52:38Z)
• Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction [62.955327005837475]
  Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. We present an efficient test-time adaptation framework for SR, named SRTTA, which is able to quickly adapt SR models to test domains with different/unknown degradation types.
  arXiv  Detail & Related papers  (2023-10-29T13:58:57Z)
• Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution [82.50210340928173]
  randomness of diffusion models results in ineffectiveness and instability, making it challenging for users to guarantee the quality of SR results. We propose a plug-and-play sampling method that owns the potential to benefit a series of diffusion-based SR methods. The quality of SR results sampled by the proposed method with fewer steps outperforms the quality of results sampled by current methods with randomness from the same pre-trained diffusion-based SR model.
  arXiv  Detail & Related papers  (2023-05-24T17:09:54Z)
• Single-photon Image Super-resolution via Self-supervised Learning [6.218646347012887]
  Single-Photon Image Super-Resolution (SPISR) aims to recover a high-resolution photon counting cube from a noisy low-resolution one by computational imaging algorithms. By extending Equivariant Imaging (EI) to single-photon data, we propose a self-supervised learning framework for the SPISR task.
  arXiv  Detail & Related papers  (2023-03-03T15:52:01Z)
• Real Image Super-Resolution using GAN through modeling of LR and HR process [20.537597542144916]
  We propose a learnable adaptive sinusoidal nonlinearities incorporated in LR and SR models by directly learn degradation distributions. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments.
  arXiv  Detail & Related papers  (2022-10-19T09:23:37Z)
• Deblurring via Stochastic Refinement [85.42730934561101]
  We present an alternative framework for blind deblurring based on conditional diffusion models. Our method is competitive in terms of distortion metrics such as PSNR.
  arXiv  Detail & Related papers  (2021-12-05T04:36:09Z)
• Toward Real-world Image Super-resolution via Hardware-based Adaptive Degradation Models [3.9037347042028254]
  Most single image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs. We propose a novel supervised method to simulate an unknown degradation process with the inclusion of prior hardware knowledge. Experiments on the real-world datasets validate that our degradation model can estimate LR images more accurately than the predetermined degradation operation.
  arXiv  Detail & Related papers  (2021-10-20T19:53:48Z)
• Designing a Practical Degradation Model for Deep Blind Image Super-Resolution [134.9023380383406]
  Single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. This paper proposes to design a more complex but practical degradation model that consists of randomly shuffled blur, downsampling and noise degradations.
  arXiv  Detail & Related papers  (2021-03-25T17:40:53Z)
• Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution [22.339751911637077]
  We propose to handle real-world SR by splitting this ill-posed problem into two comparatively more well-posed steps. First, we train a network to transform real LR images to the space of bicubically downsampled images in a supervised manner. Second, we take a generic SR network trained on bicubically downsampled images to super-resolve the transformed LR image.
  arXiv  Detail & Related papers  (2020-07-06T20:27:58Z)

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.