Deep Snapshot HDR Imaging Using Multi-exposure Color Filter Array

Suda, Takeru; Tanaka, Masayuki; Monno, Yusuke; Okutomi, Masatoshi

doi:10.1007/978-3-030-69532-3_22

Cited by 5 publications

(4 citation statements)

References 46 publications

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“…2) Single-Shot HDRI: An alternative solution is to obtain multiple exposure information of a scene via a single shot, and this solution is attractive for HDR videos [22] and full light field reconstruction [38]. Nayar and Suda proposed the concept of spatially varying exposure (SVE) in [17], [19], such that diverse pixel values of a single image are differently exposed. Two types of typical methods can be adopted to achieve SVE.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed algorithm is compared with seven single-shot HDRI algorithm [13], [18], [19], [42]- [44], [53] and two onestage joint demosaicing algorithms [24], [26] qualitatively and quantitatively on the two datasets. The denoising and superresolution in [24], [26] are disabled because they are not required by the single-shot dual-time HDRI.…”

Section: F Comparison With Existing Algorithmsmentioning

confidence: 99%

“…As such, exposure stacking is unsuitable for capturing HDR videos [14]- [16]. The singleshot HDRI was proposed to capture ghost-free HDR images with varying exposures in a single image [13], [17]- [19]. In the single-shot HDRI, the exposure of pixels varies along with different spatial locations.…”

Section: Introductionmentioning

confidence: 99%

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Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot

Xu,

Liu,

et al. 2021

Preprint

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Spatially varying exposure (SVE) is a promising choice for high-dynamic-range (HDR) imaging (HDRI). The SVEbased HDRI, which is called single-shot HDRI, is an efficient solution to avoid ghosting artifacts. However, it is very challenging to restore a full-resolution HDR image from a real-world image with SVE because: a) only one-third of pixels with varying exposures are captured by camera in a Bayer pattern, b) some of the captured pixels are over-and under-exposed. For the former challenge, a spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures. For the latter one, an exposure-guidance method is proposed against the interference from over-and under-exposed pixels. Finally, a joint demosaicing and HDRI deep learning framework is formalized to include the two novel components and to realize an end-toend single-shot HDRI. Experiments indicate that the proposed end-to-end framework avoids the problem of cumulative errors and surpasses the related state-of-the-art methods.

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Section: Literature Reviewmentioning

confidence: 99%

Section: F Comparison With Existing Algorithmsmentioning

confidence: 99%

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Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot

Xu,

Liu,

et al. 2021

Preprint

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“…Recently, deep neural networks (DNNs) have improved HDR reconstruction results [9,10,11,12]. As another one-shot approach, DNNs based on a snapshot HDR sensor have also been studied [13,14]. In [14], for example, a DNN using multiexposure color filter array images is proposed for HDR imaging.…”

Section: Introductionmentioning

confidence: 99%

Deep Snapshot Hdr Reconstruction Based On The Polarization Camera

Ting

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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The recent development of the on-chip micro-polarizer technology has made it possible to acquire four spatially aligned and temporally synchronized polarization images with the same ease of operation as a conventional camera. In this paper, we investigate the use of this sensor technology in high-dynamic-range (HDR) imaging. Specifically, observing that natural light can be attenuated differently by varying the orientation of the polarization filter, we treat the multiple images captured by the polarization camera as a set captured under different exposure times. In our approach, we first study the relationship among polarizer orientation, degree and angle of polarization of light to the exposure time of a pixel in the polarization image. Subsequently, we propose a deep snapshot HDR reconstruction framework to recover an HDR image using the polarization images. A polarized HDR dataset is created to train and evaluate our approach. We demonstrate that our approach performs favorably against state-of-the-art HDR reconstruction algorithms.

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Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging

Vien

Lee

2022

Lecture Notes in Computer Science

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Deep Snapshot HDR Imaging Using Multi-exposure Color Filter Array

Cited by 5 publications

References 46 publications

Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot

Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot

Deep Snapshot Hdr Reconstruction Based On The Polarization Camera

Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging

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