Diffusion Models, Image Super-Resolution And Everything: A Survey
- URL: http://arxiv.org/abs/2401.00736v3
- Date: Sun, 23 Jun 2024 19:32:56 GMT
- Title: Diffusion Models, Image Super-Resolution And Everything: A Survey
- Authors: Brian B. Moser, Arundhati S. Shanbhag, Federico Raue, Stanislav Frolov, Sebastian Palacio, Andreas Dengel,
- Abstract summary: Diffusion Models (DMs) have disrupted the image Super-Resolution (SR) field and closed the gap between image quality and human perceptual preferences.
DMs are easy to train and can produce very high-quality samples that exceed the realism of those produced by previous generative methods.
Despite their promising results, they also come with new challenges that need further research.
- Score: 8.869380093190628
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Diffusion Models (DMs) have disrupted the image Super-Resolution (SR) field and further closed the gap between image quality and human perceptual preferences. They are easy to train and can produce very high-quality samples that exceed the realism of those produced by previous generative methods. Despite their promising results, they also come with new challenges that need further research: high computational demands, comparability, lack of explainability, color shifts, and more. Unfortunately, entry into this field is overwhelming because of the abundance of publications. To address this, we provide a unified recount of the theoretical foundations underlying DMs applied to image SR and offer a detailed analysis that underscores the unique characteristics and methodologies within this domain, distinct from broader existing reviews in the field. This survey articulates a cohesive understanding of DM principles and explores current research avenues, including alternative input domains, conditioning techniques, guidance mechanisms, corruption spaces, and zero-shot learning approaches. By offering a detailed examination of the evolution and current trends in image SR through the lens of DMs, this survey sheds light on the existing challenges and charts potential future directions, aiming to inspire further innovation in this rapidly advancing area.
Related papers
- Retrieving Conditions from Reference Images for Diffusion Models [30.14303302029618]
We consider Subject-Driven Generation as a unified retrieval problem with diffusion models.
We introduce a novel diffusion model architecture, named RetriNet, designed to address and solve these problems.
We also propose a research and friendly dataset, RetriBooru, to study a more difficult problem, concept composition.
arXiv Detail & Related papers (2023-12-05T06:04:16Z) - Infrared Image Super-Resolution via GAN [3.2199000920848486]
We provide a brief overview of the application of generative models in the domain of infrared (IR) image super-resolution.
We propose potential areas for further investigation and advancement in the application of generative models for IR image super-resolution.
arXiv Detail & Related papers (2023-12-01T16:16:46Z) - A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics.
This survey delves into the realm of interpretable cross-modal reasoning (I-CMR)
This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
arXiv Detail & Related papers (2023-09-05T05:06:48Z) - Diffusion Models for Image Restoration and Enhancement -- A
Comprehensive Survey [96.99328714941657]
We present a comprehensive review of recent diffusion model-based methods on image restoration.
We classify and emphasize the innovative designs using diffusion models for both IR and blind/real-world IR.
We propose five potential and challenging directions for the future research of diffusion model-based IR.
arXiv Detail & Related papers (2023-08-18T08:40:38Z) - 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) - Guided Depth Map Super-resolution: A Survey [88.54731860957804]
Guided depth map super-resolution (GDSR) aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image.
A myriad of novel and effective approaches have been proposed recently, especially with powerful deep learning techniques.
This survey is an effort to present a comprehensive survey of recent progress in GDSR.
arXiv Detail & Related papers (2023-02-19T15:43:54Z) - Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances [3.966405801901351]
Super-Resolution (SR) has become a thriving research area.
Despite promising results, the field still faces challenges that require further research.
This review is ultimately aimed at helping researchers to push the boundaries of DL applied to SR.
arXiv Detail & Related papers (2022-09-27T03:28:34Z) - Causal Reasoning Meets Visual Representation Learning: A Prospective
Study [117.08431221482638]
Lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models.
Inspired by the strong inference ability of human-level agents, recent years have witnessed great effort in developing causal reasoning paradigms.
This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods.
arXiv Detail & Related papers (2022-04-26T02:22:28Z) - Blind Image Super-Resolution: A Survey and Beyond [43.316988709621604]
Blind image super-resolution (SR) aims to super-resolve low-resolution images with unknown degradation.
Despite years of efforts, it still remains as a challenging research problem.
This paper serves as a systematic review on recent progress in blind image SR.
arXiv Detail & Related papers (2021-07-07T07:38:14Z) - Kernel Agnostic Real-world Image Super-resolution [82.3963188538938]
We introduce a new kernel agnostic SR framework to deal with real-world image SR problem.
In the proposed framework, the degradation kernels and noises are adaptively modeled rather than explicitly specified.
The experiments validate the effectiveness of the proposed framework on multiple real-world datasets.
arXiv Detail & Related papers (2021-04-19T01:51:21Z)
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.