Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping

• URL: http://arxiv.org/abs/2006.11708v2
• Date: Tue, 30 Jun 2020 18:14:13 GMT
• Title: Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping
• Authors: Vasileios Lioutas
• Abstract summary: We argue that, first and foremost, the problem of SISR is an one-to-many mapping problem between the low-resolution and all possible candidate high-resolution images. We propose a model that learns how to transform high-resolution images to low-resolution images that resemble realistically taken low-resolution photos.
• Score: 6.302374268077337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Several methods have recently been proposed for the Single Image Super-Resolution (SISR) problem. The current methods assume that a single low-resolution image can only yield a single high-resolution image. In addition, all of these methods use low-resolution images that were artificially generated through simple bilinear down-sampling. We argue that, first and foremost, the problem of SISR is an one-to-many mapping problem between the low-resolution and all possible candidate high-resolution images and we address the challenging task of learning how to realistically degrade and down-sample high-resolution images. To circumvent this problem, we propose SR-NAM which utilizes the Non-Adversarial Mapping (NAM) technique. Furthermore, we propose a degradation model that learns how to transform high-resolution images to low-resolution images that resemble realistically taken low-resolution photos. Finally, some qualitative results for the proposed method along with the weaknesses of SR-NAM are included.

Related papers

• LoLiSRFlow: Joint Single Image Low-light Enhancement and Super-resolution via Cross-scale Transformer-based Conditional Flow [8.929704596997913]
  We propose a normalizing flow network, dubbed LoLiSRFLow, to consider the degradation mechanism inherent in Low-Light Enhancement (LLE) and Super- Resolution (SR) LoLiSRFLow learns the conditional probability distribution over a variety of feasible solutions for high-resolution well-exposed images. We also propose a synthetic dataset modeling the realistic low-light low-resolution degradation, named DFSR-LLE, containing 7100 low-resolution dark-light/high-resolution normal sharp pairs.
  arXiv  Detail & Related papers  (2024-02-29T05:40:43Z)
• How Real is Real: Evaluating the Robustness of Real-World Super Resolution [0.0]
  Super-resolution is a well-known problem as most methods rely on the downsampling method performed on the high-resolution image to form the low-resolution image to be known. We will evaluate multiple state-of-the-art super-resolution methods and gauge their performance when presented with various types of real-life images. We will present a potential solution to alleviate the generalization problem which is imminent in most state-of-the-art super-resolution models.
  arXiv  Detail & Related papers  (2022-10-22T18:53:45Z)
• Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification [49.57112924976762]
  Cross-resolution person re-identification problem aims to match low-resolution (LR) query identity images against high resolution (HR) gallery images. It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras. This paper explores an alternative SR-free paradigm to directly compare HR and LR images via a dynamic metric, which is adaptive to the resolution of a query image.
  arXiv  Detail & Related papers  (2022-07-09T03:49:51Z)
• Multi-Modality Image Super-Resolution using Generative Adversarial Networks [0.0]
  We propose a solution to the joint problem of image super-resolution and multi-modality image-to-image translation. The problem can be stated as the recovery of a high-resolution image in a modality, given a low-resolution observation of the same image in an alternative modality.
  arXiv  Detail & Related papers  (2022-06-18T12:19:31Z)
• SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation [65.88321755969677]
  Deep CNNs have achieved significant successes in image processing and its applications, including single image super-resolution. Recent approaches extend the scope to real-valued upsampling factors. We propose the SRWarp framework to further generalize the SR tasks toward an arbitrary image transformation.
  arXiv  Detail & Related papers  (2021-04-21T02:50:41Z)
• A Generative Model for Hallucinating Diverse Versions of Super Resolution Images [0.3222802562733786]
  We are tackling in this work the problem of obtaining different high-resolution versions from the same low-resolution image using Generative Adversarial Models. Our learning approach makes use of high frequencies available in the training high-resolution images for preserving and exploring in an unsupervised manner.
  arXiv  Detail & Related papers  (2021-02-12T17:11:42Z)
• Image Restoration by Solving IVP [7.26562478548988]
  We introduce a new formulation for image super-resolution to solve arbitrary scale image super-resolution methods. Based on the proposed new SR formulation, we can not only super-resolve images with multiple scales, but also find a new way to analyze the performance of super-resolving process.
  arXiv  Detail & Related papers  (2021-01-22T07:59:48Z)
• Image Matching with Scale Adjustment [57.18604132027697]
  We show how to represent and extract interest points at variable scales. We devise a method allowing the comparison of two images at two different resolutions.
  arXiv  Detail & Related papers  (2020-12-10T11:03:25Z)
• SRFlow: Learning the Super-Resolution Space with Normalizing Flow [176.07982398988747]
  Super-resolution is an ill-posed problem, since it allows for multiple predictions for a given low-resolution image. We propose SRFlow: a normalizing flow based super-resolution method capable of learning the conditional distribution of the output. Our model is trained in a principled manner using a single loss, namely the negative log-likelihood.
  arXiv  Detail & Related papers  (2020-06-25T06:34:04Z)
• Invertible Image Rescaling [118.2653765756915]
  We develop an Invertible Rescaling Net (IRN) to produce visually-pleasing low-resolution images. We capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process.
  arXiv  Detail & Related papers  (2020-05-12T09:55:53Z)
• PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models [77.32079593577821]
  PULSE (Photo Upsampling via Latent Space Exploration) generates high-resolution, realistic images at resolutions previously unseen in the literature. Our method outperforms state-of-the-art methods in perceptual quality at higher resolutions and scale factors than previously possible.
  arXiv  Detail & Related papers  (2020-03-08T16:44:31Z)

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.