# Advancements and Trends in Ultra-High-Resolution Image Processing: An Overview

arXiv: https://arxiv.org/abs/2312.00250 · HTML: https://mlsanity.com/paper/2312.00250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 40 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 20 of 20)

- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022)
- [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020)
- [Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement](https://mlsanity.com/paper/2302.11831/) (2023)
- [AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement](https://mlsanity.com/paper/2204.13983/) (2022)
- [Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables](https://mlsanity.com/paper/2108.08697/) (2021)
- [High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network](https://mlsanity.com/paper/2105.09188/) (2021)
- [SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement](https://mlsanity.com/paper/2207.08351/) (2022)
- [Bilateral Grid Learning for Stereo Matching Networks](https://mlsanity.com/paper/2101.01601/) (2021)
- [4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement](https://mlsanity.com/paper/2209.01749/) (2022)
- [Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer](https://mlsanity.com/paper/2004.10955/) (2020)
- [Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing](https://mlsanity.com/paper/2207.09935/) (2022)
- [An All-in-One Network for Dehazing and Beyond](https://mlsanity.com/paper/1707.06543/) (2017)
- [Five A+ Network: You Only Need 9K Parameters for Underwater Image Enhancement](https://mlsanity.com/paper/2305.08824/) (2023)
- [MEFLUT: Unsupervised 1D Lookup Tables for Multi-exposure Image Fusion](https://mlsanity.com/paper/2309.11847/) (2023)
- [Single UHD Image Dehazing via Interpretable Pyramid Network](https://mlsanity.com/paper/2202.08589/) (2022)
- [4K-Resolution Photo Exposure Correction at 125 FPS with ~8K Parameters](https://mlsanity.com/paper/2311.08759/) (2023)
- [Direction-aware Video Demoireing with Temporal-guided Bilateral Learning](https://mlsanity.com/paper/2308.13388/) (2023)
- [Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting](https://mlsanity.com/paper/2303.10096/) (2023)
- [Ultra-High-Definition Image Deblurring via Multi-scale Cubic-Mixer](https://mlsanity.com/paper/2206.03678/) (2022)

## Abstract (arXiv)

Currently, to further improve visual enjoyment, Ultra-High-Definition (UHD) images are catching wide attention. Here, UHD images are usually referred to as having a resolution greater than or equal to 3840 × 2160. However, since the imaging equipment is subject to environmental noise or equipment jitter, UHD images are prone to contrast degradation, blurring, low dynamic range, etc. To address these issues, a large number of algorithms for UHD image enhancement have been proposed. In this paper, we introduce the current state of UHD image enhancement from two perspectives, one is the application field and the other is the technology. In addition, we briefly explore its trends.