HDR Reconstruction
19 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in HDR Reconstruction
MSU HDR Video Reconstruction Benchmark
SI-HDR
Most implemented papers
Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging
viengiaan/EDWL
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European Conference on Computer Vision (ECCV) 2022
We propose a novel single-shot high dynamic range (HDR) imaging algorithm based on exposure-aware dynamic weighted learning, which reconstructs an HDR image from a spatially varying exposure (SVE) raw image.
Perceptual Image Enhancement for Smartphone Real-Time Applications
mv-lab/AISP
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Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks.
Single-Image HDR Reconstruction by Multi-Exposure Generation
vinairesearch/single_image_hdr
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In this work, we propose a weakly supervised learning method that inverts the physical image formation process for HDR reconstruction via learning to generate multiple exposures from a single image.
LHDR: HDR Reconstruction for Legacy Content using a Lightweight DNN
andreguo/lhdr
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High dynamic range (HDR) image is widely-used in graphics and photography due to the rich information it contains.
High Dynamic Range Image Reconstruction via Deep Explicit Polynomial Curve Estimation
Besides, since all current datasets do not provide the corresponding relationship between the tone mapping function and the LDR image, we construct a new dataset with both synthetic and real images.
RawHDR: High Dynamic Range Image Reconstruction from a Single Raw Image
Unlike existing methods, the core idea of this work is to incorporate more informative Raw sensor data to generate HDR images, aiming to recover scene information in hard regions (the darkest and brightest areas of an HDR scene).
Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes
The color component is estimated from aligned multi-exposure images, while the structure one is generated through a structure-focused network that is supervised by the color component and an input reference (\eg, medium-exposure) image.
ParamISP: Learned Forward and Inverse ISPs using Camera Parameters
woo525/ParamISP
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RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs.
Single-Image HDR Reconstruction Assisted Ghost Suppression and Detail Preservation Network for Multi-Exposure HDR Imaging
lhf12278/samhdr
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This network, comprising single-frame HDR reconstruction with enhanced stop image (SHDR-ESI) and SHDR-ESI-assisted multi-exposure HDR reconstruction (SHDRA-MHDR), effectively leverages the ghost-free characteristic of single-frame HDR reconstruction and the detail-enhancing capability of ESI in oversaturated areas.
