HDR Reconstruction from Bracketed Exposures and Events

Shaw, Richard E.; Catley-Chandar, Sibi; Leonardis, Aleš; Pérez-Pellitero, Eduardo

doi:10.48550/arxiv.2203.14825

Cited by 2 publications

(2 citation statements)

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“…Messikommer et al [20] first combined bracketed LDR images and synchronized events for HDR imaging, demonstrating enhanced robustness in handling noise and ghosts. Richard et al [39] introduced a novel event-to-image feature distillation module, directly transforming event features into the image feature space without relying on an intermediary intensity image. Yang et al [40] presented a multi-modal learning framework for reconstructing HDR videos from hybrid inputs of LDR videos and events.…”

Section: Event-based Hdr Reconstructionmentioning

confidence: 99%

ERS-HDRI: Event-Based Remote Sensing HDR Imaging

Li,

Cheng,

Zeng

et al. 2024

Remote Sensing

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High dynamic range imaging (HDRI) is an essential task in remote sensing, enhancing low dynamic range (LDR) remote sensing images and benefiting downstream tasks, such as object detection and image segmentation. However, conventional frame-based HDRI methods may encounter challenges in real-world scenarios due to the limited information inherent in a single image captured by conventional cameras. In this paper, an event-based remote sensing HDR imaging framework is proposed to address this problem, denoted as ERS-HDRI, which reconstructs the remote sensing HDR image from a single-exposure LDR image and its concurrent event streams. The proposed ERS-HDRI leverages a coarse-to-fine framework, incorporating the event-based dynamic range enhancement (E-DRE) network and the gradient-enhanced HDR reconstruction (G-HDRR) network. Specifically, to efficiently achieve dynamic range fusion from different domains, the E-DRE network is designed to extract the dynamic range features from LDR frames and events and perform intra- and cross-attention operations to adaptively fuse multi-modal data. A denoise network and a dense feature fusion network are then employed for the generation of the coarse, clean HDR image. Then, the G-HDRR network, with its gradient enhancement module and multiscale fusion module, performs structure enforcement on the coarse HDR image and generates a fine informative HDR image. In addition, this work introduces a specialized hybrid imaging system and a novel, real-world event-based remote sensing HDRI dataset that contains aligned remote sensing LDR images, remote sensing HDR images, and concurrent event streams for evaluation. Comprehensive experiments have demonstrated the effectiveness of the proposed method. Specifically, it improves state-of-the-art PSNR by about 30% and the SSIM score by about 9% on the real-world dataset.

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Section: Event-based Hdr Reconstructionmentioning

confidence: 99%

ERS-HDRI: Event-Based Remote Sensing HDR Imaging

Li,

Cheng,

Zeng

et al. 2024

Remote Sensing

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“…After flow estimation, the FlexHDR model addresses uncertainties caused by exposure and alignment via an attention network, and the final HDR is generated by a multi-stage fusion-based merging network. Other learning-based methods can be found at [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ].…”

Section: Related Workmentioning

confidence: 99%

Exploiting Light Polarization for Deep HDR Imaging from a Single Exposure

Pistellato

Fatima

Wimmer

2023

Sensors

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In computational photography, high dynamic range (HDR) imaging refers to the family of techniques used to recover a wider range of intensity values compared to the limited range provided by standard sensors. Classical techniques consist of acquiring a scene-varying exposure to compensate for saturated and underexposed regions, followed by a non-linear compression of intensity values called tone mapping. Recently, there has been a growing interest in estimating HDR images from a single exposure. Some methods exploit data-driven models trained to estimate values outside the camera’s visible intensity levels. Others make use of polarimetric cameras to reconstruct HDR information without exposure bracketing. In this paper, we present a novel HDR reconstruction method that employs a single PFA (polarimetric filter array) camera with an additional external polarizer to increase the scene’s dynamic range across the acquired channels and to mimic different exposures. Our contribution consists of a pipeline that effectively combines standard HDR algorithms based on bracketing and data-driven solutions designed to work with polarimetric images. In this regard, we present a novel CNN (convolutional neural network) model that exploits the underlying mosaiced pattern of the PFA in combination with the external polarizer to estimate the original scene properties, and a second model designed to further improve the final tone mapping step. The combination of such techniques enables us to take advantage of the light attenuation given by the filters while producing an accurate reconstruction. We present an extensive experimental section in which we validate the proposed method on both synthetic and real-world datasets specifically acquired for the task. Quantitative and qualitative results show the effectiveness of the approach when compared to state-of-the-art methods. In particular, our technique exhibits a PSNR (peak signal-to-noise ratio) on the whole test set equal to 23 dB, which is 18% better with respect to the second-best alternative.

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HDR Reconstruction from Bracketed Exposures and Events

Cited by 2 publications

References 0 publications

ERS-HDRI: Event-Based Remote Sensing HDR Imaging

ERS-HDRI: Event-Based Remote Sensing HDR Imaging

Exploiting Light Polarization for Deep HDR Imaging from a Single Exposure

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