computer-vision-9

HDR Reconstruction

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Libraries

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Use these libraries to find hdr-reconstruction-9 models and implementations

Datasets

MSU HDR Video Reconstruction Benchmark

SI-HDR

Subtasks

Multi-Exposure Image Fusion

Most implemented papers

HDR image reconstruction from a single exposure using deep CNNs

Rafał K. Mantiuk, Gabriel Eilertsen, Jonas Unger, J. Kronander, G. Denes•Thu Oct 19 2017

This paper addresses the problem of predicting information that have been lost in saturated image areas, in order to enable HDR reconstruction from a single exposure, and proposes a deep convolutional neural network (CNN) that is specifically designed taking into account the challenges in predicting HDR values.

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Deep HDR Imaging via A Non-Local Network

Javen Qinfeng Shi, Qingsen Yan, Yanning Zhang, Lei Zhang, Yu Liu, Jinqiu Sun•Sun Feb 09 2020

One of the most challenging problems in reconstructing a high dynamic range (HDR) image from multiple low dynamic range (LDR) inputs is the ghosting artifacts caused by the object motion across different inputs. When the object motion is slight, most existing methods can well suppress the ghosting artifacts through aligning LDR inputs based on optical flow or detecting anomalies among them. However, they often fail to produce satisfactory results in practice, since the real object motion can be very large. In this study, we present a novel deep framework, termed NHDRRnet, which adopts an alternative direction and attempts to remove ghosting artifacts by exploiting the non-local correlation in inputs. In NHDRRnet, we first adopt an Unet architecture to fuse all inputs and map the fusion results into a low-dimensional deep feature space. Then, we feed the resultant features into a novel global non-local module which reconstructs each pixel by weighted averaging all the other pixels using the weights determined by their correspondences. By doing this, the proposed NHDRRnet is able to adaptively select the useful information (e.g., which are not corrupted by large motions or adverse lighting conditions) in the whole deep feature space to accurately reconstruct each pixel. In addition, we also incorporate a triple-pass residual module to capture more powerful local features, which proves to be effective in further boosting the performance. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed NDHRnet in terms of suppressing the ghosting artifacts in HDR reconstruction, especially when the objects have large motions.

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Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline

Ming-Hsuan Yang, Wei-Sheng Lai, Yung-Yu Chuang, Yu-Lun Liu, Yu-Sheng Chen, Yi-Lung Kao, Jia-Bin Huang•Wed Apr 01 2020

This work model the HDR-to-LDR image formation pipeline as the dynamic range clipping, non-linear mapping from a camera response function, and quantization, and proposes to learn three specialized CNNs to reverse these steps.

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HDR-GAN: HDR Image Reconstruction From Multi-Exposed LDR Images With Large Motions

Wenzhong Guo, Rynson W. H. Lau, Yuzhen Niu, Jianbin Wu, Wenxi Liu•Thu Jul 02 2020

This work proposes a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images, and achieves state-of-the-art reconstruction performance over the prior HDR methods on diverse scenes.

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HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset

Guanying Chen, Kwan-Yee Kenneth Wong, Lei Zhang, Shi Guo, Zhetong Liang, Chaofeng Chen•Fri Mar 26 2021

This work introduces a coarse-to-fine deep learning framework for HDR video reconstruction, which outperforms previous state-of-the-art methods and conducts more sophisticated alignment and temporal fusion in the feature space of the coarse HDR video to produce better reconstruction.

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HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization

Chao Dong, Y. Qiao, Xiangyu Chen, Yihao Liu, Zhengwen Zhang•Wed May 26 2021

This work proposes 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, which achieves the state-of-the-art performance in quantitative comparisons and visual quality.

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NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results

A. Leonardis, Radu Timofte, Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Xiangyu Chen•Mon May 31 2021

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021.

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How to cheat with metrics in single-image HDR reconstruction

Rafał K. Mantiuk, Param Hanji, Gabriel Eilertsen, Saghi Hajisharif, Jonas Unger, Apostolia Tsirikoglou•Wed Aug 18 2021

It is found that methods that do not even reconstruct HDR information can compete with state-of-the-art deep learning methods, and that SI-HDR reconstruction needs better evaluation protocols.

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Luminance Attentive Networks for HDR Image and Panorama Reconstruction

Chunxia Xiao, Chengjiang Long, Hanning Yu, Wentao Liu, Bo Dong, Qin Zou•Mon Sep 13 2021

Extensive experiments show that the proposed approach LANet can reconstruct visually convincing HDR images and demonstrate its superiority over state‐of‐the‐art approaches in terms of all metrics in inverse tone mapping.

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Comparison of single image HDR reconstruction methods — the caveats of quality assessment

Rafał K. Mantiuk, Param Hanji, Gabriel Eilertsen, Saghi Hajisharif, Jonas Unger•Tue Jul 26 2022

This work compared six recent single image HDR reconstruction methods in a subjective image quality experiment on an HDR display and found that only two methods produced results that are, on average, more preferred than the unprocessed single exposure images.

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