Deep Learning-Driven Ultra-High-Definition Image Restoration: A Survey

• URL: http://arxiv.org/abs/2505.16161v1
• Date: Thu, 22 May 2025 03:03:53 GMT
• Title: Deep Learning-Driven Ultra-High-Definition Image Restoration: A Survey
• Authors: Liyan Wang, Weixiang Zhou, Cong Wang, Kin-Man Lam, Zhixun Su, Jinshan Pan,
• Abstract summary: Ultra-high-definition (UHD) image restoration aims to specifically solve the problem of quality degradation in ultra-high-resolution images.<n>Recent advancements in this field are predominantly driven by deep learning-based innovations.<n>We systematically review recent progress in UHD image restoration, covering various aspects ranging from dataset construction to algorithm design.
• Score: 40.76611383104126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ultra-high-definition (UHD) image restoration aims to specifically solve the problem of quality degradation in ultra-high-resolution images. Recent advancements in this field are predominantly driven by deep learning-based innovations, including enhancements in dataset construction, network architecture, sampling strategies, prior knowledge integration, and loss functions. In this paper, we systematically review recent progress in UHD image restoration, covering various aspects ranging from dataset construction to algorithm design. This serves as a valuable resource for understanding state-of-the-art developments in the field. We begin by summarizing degradation models for various image restoration subproblems, such as super-resolution, low-light enhancement, deblurring, dehazing, deraining, and desnowing, and emphasizing the unique challenges of their application to UHD image restoration. We then highlight existing UHD benchmark datasets and organize the literature according to degradation types and dataset construction methods. Following this, we showcase major milestones in deep learning-driven UHD image restoration, reviewing the progression of restoration tasks, technological developments, and evaluations of existing methods. We further propose a classification framework based on network architectures and sampling strategies, helping to clearly organize existing methods. Finally, we share insights into the current research landscape and propose directions for further advancements. A related repository is available at https://github.com/wlydlut/UHD-Image-Restoration-Survey.

Related papers

• A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution [30.62413133817583]
  This paper presents a systematic review of recent progress in omnidirectional image and video super-resolution.<n>We introduce a new dataset, 360Insta, that comprises authentically degraded omnidirectional images and videos.<n>We conduct comprehensive qualitative and quantitative evaluations of existing methods on both public datasets and our proposed dataset.
  arXiv  Detail & Related papers  (2025-06-07T08:24:44Z)
• Decouple to Reconstruct: High Quality UHD Restoration via Active Feature Disentanglement and Reversible Fusion [77.08942160610478]
  Ultra-high-definition (UHD) image restoration often faces computational bottlenecks and information loss due to its extremely high resolution.<n>We propose a Controlled Differential Disentangled VAE that discards easily recoverable background information while encoding more difficult-to-recover degraded information into latent space.<n>Our method effectively alleviates the information loss problem in VAE models while ensuring computational efficiency, significantly improving the quality of UHD image restoration, and achieves state-of-the-art results in six UHD restoration tasks with only 1M parameters.
  arXiv  Detail & Related papers  (2025-03-17T02:55:18Z)
• State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications [0.0]
  Image Super-Resolution aims to recover a high-resolution image from its low-resolution counterpart.<n>Recent advancements in transformer-based methods have remolded image super-resolution.
  arXiv  Detail & Related papers  (2025-01-14T05:43:59Z)
• A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing [5.6223397629993626]
  We propose a novel progressive restoration network for high-order degradation imaging (HDI-PRNet)<n>Our method achieves superior performance on both synthetic and real remote sensing images.
  arXiv  Detail & Related papers  (2024-12-10T05:08:39Z)
• A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends [67.43992456058541]
  Image restoration (IR) refers to the process of improving visual quality of images while removing degradation, such as noise, blur, weather effects, and so on. Traditional IR methods typically target specific types of degradation, which limits their effectiveness in real-world scenarios with complex distortions. The all-in-one image restoration (AiOIR) paradigm has emerged, offering a unified framework that adeptly addresses multiple degradation types.
  arXiv  Detail & Related papers  (2024-10-19T11:11:09Z)
• Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration [19.87693298262894]
  We propose Diff-Restorer, a universal image restoration method based on the diffusion model. We utilize the pre-trained visual language model to extract visual prompts from degraded images. We also design a Degradation-aware Decoder to perform structural correction and convert the latent code to the pixel domain.
  arXiv  Detail & Related papers  (2024-07-04T05:01:10Z)
• SSP-IR: Semantic and Structure Priors for Diffusion-based Realistic Image Restoration [20.873676111265656]
  SSP-IR aims to fully exploit semantic and structure priors from low-quality images.<n>Our method outperforms other state-of-the-art methods overall on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-07-04T04:55:14Z)
• InstructIR: High-Quality Image Restoration Following Human Instructions [61.1546287323136]
  We present the first approach that uses human-written instructions to guide the image restoration model. Our method, InstructIR, achieves state-of-the-art results on several restoration tasks.
  arXiv  Detail & Related papers  (2024-01-29T18:53:33Z)
• Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning [53.85892601302974]
  We propose an effective image dehazing method named Hierarchical Contrastive Dehazing (HCD) HCD consists of a hierarchical dehazing network (HDN) and a novel hierarchical contrastive loss (HCL)
  arXiv  Detail & Related papers  (2022-12-22T03:57:06Z)
• A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal [177.21001709272144]
  Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images. This paper comprehensively surveys recent advances in deep learning techniques for face restoration.
  arXiv  Detail & Related papers  (2022-11-05T07:08:15Z)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)

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.