Inverse-Tone-Mapping
19 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Inverse-Tone-Mapping
| Trend | Dataset | Best Model | Paper | Code | Compare |
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MSU HDR Video Reconstruction Benchmark
Most implemented papers
HDR image reconstruction from a single exposure using deep CNNs
We demonstrate that our approach can reconstruct high-resolution visually convincing HDR results in a wide range of situations, and that it generalizes well to reconstruction of images captured with arbitrary and low-end cameras that use unknown camera response functions and post-processing.
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content
This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet.
Deep Recursive HDRI: Inverse Tone Mapping using Generative Adversarial Networks
Siyeong-Lee/Deep_Recursive_HDRI
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ECCV 2018
Because most images have a low dynamic range, recovering the lost dynamic range from a single low dynamic range image is still prevalent.
Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications
Joint SR and ITM is an intricate task, where high frequency details must be restored for SR, jointly with the local contrast, for ITM.
JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video
Joint learning of super-resolution (SR) and inverse tone-mapping (ITM) has been explored recently, to convert legacy low resolution (LR) standard dynamic range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for the growing need of UHD HDR TV/broadcasting applications.
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
alex04072000/SingleHDR
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CVPR 2020
We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization.
A Two-stage Deep Network for High Dynamic Range Image Reconstruction
sharif-apu/twostageHDR_NTIRE21
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Notably, our proposed method aims to reconstruct an HDR image without knowing hardware information, including camera response function (CRF) and exposure settings.
HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization
chxy95/HDRUNet
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In this work, we propose a novel learning-based approach using a spatially dynamic encoder-decoder network, HDRUNet, to learn an end-to-end mapping for single image HDR reconstruction with denoising and dequantization.
A New Journey from SDRTV to HDRTV
However, most available resources are still in standard dynamic range (SDR).
Luminance Attentive Networks for HDR Image and Panorama Reconstruction
lwt3437/lanet
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Based on this observation, we propose a novel normalization method called " HDR calibration " for HDR images stored in relative luminance, calibrating HDR images into a similar luminance scale according to the LDR images.
