4KLSDB: A Large-Scale Dataset for 4K Image Restoration and Generation

• URL: http://arxiv.org/abs/2605.24762v1
• Date: Sat, 23 May 2026 22:49:04 GMT
• Title: 4KLSDB: A Large-Scale Dataset for 4K Image Restoration and Generation
• Abstract summary: Current publicly available datasets lack both the native 4K resolution and the extensive scale necessary for training state-of-the-art models.<n>We introduce a 4K Large Scale dataset and Benchmark (4KLSDB) consisting of 129,484 carefully curated 4K resolution images spanning multiple categories.<n>We demonstrate 4KLSDB's effectiveness by training representative super-resolution and diffusion models, observing significant improvements on native 4K benchmarks.
• Score: 23.288582652884642
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-resolution datasets are essential for advancing super-resolution (SR) and text-to-image (T2I) diffusion research. However, current publicly available datasets lack both the native 4K resolution and the extensive scale necessary for training state-of-the-art models. To address this gap, we introduce a 4K Large Scale Dataset and Benchmark (4KLSDB), a large-scale, diverse dataset consisting of 129,484 carefully curated 4K resolution images spanning multiple categories such as nature, urban scenes, people, food, artwork, and CGI, alongside distinct validation and test sets containing 2,000 and 1,984 images respectively. Images were sourced from established open datasets including Photo Concept Bucket, Laion2B, and PD12M. 4KLSDB underwent rigorous multi-stage automated filtering and annotation pipelines involving both human annotators and Large Multimodal Models (LMMs) to ensure high aesthetic quality and dataset consistency. We demonstrate 4KLSDB's effectiveness by training representative super-resolution and diffusion models, observing significant improvements in performance on native 4K benchmarks. Comprehensive experiments illustrate a positive correlation between training on true 4K resolution data and improved fidelity in image restoration task, especially on 4K resolution. We provide the research community a valuable resource to drive progress toward genuinely high-fidelity image synthesis and restoration by providing 4KLSDB. Our project page is available at: https://4klsdb.github.io/.

Related papers

• Human4K: A Large-Scale 4K Multi-View Mocap Dataset for Whole-Body 3D Human Reconstruction [52.66661102115907]
  We present Human4K, a large-scale 4K multi-view whole-body human reconstruction dataset with mocap-accurate SMPL-X annotations.<n>Human4K contains over six million 4K images captured by an eight-view high-resolution camera system synchronized with a professional Vicon motion capture setup.<n> Experimental results show that training with Human4K consistently improves whole-body reconstruction on standard benchmarks.
  arXiv  Detail & Related papers  (2026-07-15T09:44:25Z)
• VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset [35.077683100413076]
  We introduce VINS-120K, the first large-scale dataset for instruction-based UHR image editing.<n>We develop a high-frequency-aware post-adaptation strategy to extend pretrained non-high-resolution models to the UHR regime.<n>Experiments confirm that our work improves fine-grained detail synthesis and texture realism in UHR image editing.
  arXiv  Detail & Related papers  (2026-05-22T11:33:43Z)
• Lens: Rethinking Training Efficiency for Foundational Text-to-Image Models [88.94987156920575]
  We introduce Lens, a T2I model that achieves performance competitive with, and in several cases surpassing, state-of-the-art models with more than 6B parameters.<n>For example, Lens requires only about 19.3% of the training compute used by Z-Image.<n>Lens generates a 10242 image in 3.15 seconds on a single NVIDIA H100 GPU, while its distilled turbo version performs 4-step generation in 0.84 seconds.
  arXiv  Detail & Related papers  (2026-05-20T17:59:58Z)
• UltraFlux: Data-Model Co-Design for High-quality Native 4K Text-to-Image Generation across Diverse Aspect Ratios [11.829523789114377]
  We introduce UltraFlux, a Flux-based DiT trained at 4K on MultiAspect-4K-1M.<n>On the model side, UltraFlux couples Resonance 2D RoPE with YaRN for training-window, frequency-, and AR-aware positional encoding at 4K.<n>On the Aesthetic-Eval at 4096 benchmark and multi-AR 4K settings, UltraFlux consistently outperforms strong open-source baselines across fidelity, aesthetic, and alignment metrics.
  arXiv  Detail & Related papers  (2025-11-22T13:07:21Z)
• 4KAgent: Agentic Any Image to 4K Super-Resolution [62.99433518118836]
  We present 4KAgent, a super-resolution generalist system designed to upscale any image to 4K resolution (and even higher, if applied iteratively)<n>4KAgent comprises three core components: (1) Profiling, a module that customizes the 4KAgent pipeline based on bespoke use cases; (2) A Perception Agent, which leverages vision-language models alongside image quality assessment experts to analyze the input image and make a tailored restoration plan; and (3) A Restoration Agent, which executes the plan, following a quality-driven mixture-of-expert policy to select the optimal output for each step.<n>We rigorously evaluate our 4KAgent
  arXiv  Detail & Related papers  (2025-07-09T17:59:19Z)
• Ultra-High-Resolution Image Synthesis: Data, Method and Evaluation [21.46605047406198]
  Aesthetic-4K dataset is curated for comprehensive research on ultra-high-resolution image synthesis.<n>Diffusion-4K is an innovative framework for the direct generation of ultra-high-resolution images.
  arXiv  Detail & Related papers  (2025-06-02T05:19:40Z)
• Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models [21.46605047406198]
  Diffusion-4K is a novel framework for direct ultra-high-resolution image synthesis using text-to-image diffusion models.<n>We construct Aesthetic-4K, a comprehensive benchmark for ultra-high-resolution image generation.<n>We propose a wavelet-based fine-tuning approach for direct training with 4K images, applicable to various latent diffusion models.
  arXiv  Detail & Related papers  (2025-03-24T05:25:07Z)
• InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD [129.9919468062788]
  InternLM-XComposer2-4KHD is a groundbreaking exploration into elevating LVLM resolution capabilities up to 4K HD (3840 x 1600) and beyond. This research advances the patch division paradigm by introducing a novel extension: dynamic resolution with automatic patch configuration. Our research demonstrates that scaling training resolution up to 4K HD leads to consistent performance enhancements without hitting the ceiling of potential improvements.
  arXiv  Detail & Related papers  (2024-04-09T17:59:32Z)
• 4K4D: Real-Time 4D View Synthesis at 4K Resolution [86.6582179227016]
  This paper targets high-fidelity and real-time view of dynamic 3D scenes at 4K resolution. We propose a 4D point cloud representation that supports hardwareization and enables unprecedented rendering speed. Our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an 4090 GPU.
  arXiv  Detail & Related papers  (2023-10-17T17:57:38Z)
• 4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free Images [12.402054374952485]
  We develop a novel method to simulate 4K hazy images from clear images, which first estimates the scene depth, simulates the light rays and object reflectance, then migrates the synthetic images to real domains by using a GAN. We wrap these synthesized images into a benchmark called the 4K-HAZE dataset. The most appealing aspect of our approach is the capability to run a 4K image on a single GPU with 24G RAM in real-time (33fps)
  arXiv  Detail & Related papers  (2023-03-28T09:39:29Z)
• Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing [71.62289021118983]
  We present an efficient baseline model ESDNet for tackling 4K moire images, wherein we build a semantic-aligned scale-aware module to address the scale variation of moire patterns. Our approach outperforms state-of-the-art methods by a large margin while being much more lightweight.
  arXiv  Detail & Related papers  (2022-07-20T14:20:52Z)

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.