Multi-exposure high dynamic range imaging with informative content enhanced network

Pan, Zhiyong; Yu, Mei; Jiang, Gangyi; Xu, Haiyong; Peng, Zongju; Chen, Fen

doi:10.1016/j.neucom.2019.12.093

Cited by 24 publications

(10 citation statements)

References 17 publications

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“…Interestingly, to better align the multi-exposed LDR images, [172] proposed a multiscale contextual attention module to obtain multiscale attention feature maps for alignment. By contrast, [173] focused on static multiexposure HDR image reconstruction and proposed the use of channel-wise attention, similar to [171], to learn the channel-wise statistics for the under-/over-exposed regions in the LDR images. Discussion: From the aforementioned analysis, we identified that the attention mechanism is effective for multi-exposure mergers regardless of dynamic or static scenes.…”

Section: Hdr Imaging With the Attention Mechanismmentioning

confidence: 99%

“…Using the attention mechanism leads to fewer ghost artifacts than when using an optical flow. However, as pointed out by [173], using attention mechanisms still has limitations for LDR images with fewer sharp edges and textures. Increasing the effectiveness of attention mechanisms requires more parameters for the reconstruction networks, thus increasing the computational cost.…”

Section: Hdr Imaging With the Attention Mechanismmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

Wang

Yoon

2021

Preprint

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High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five categories based on (1) number/domain of input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel learning strategies, and (5) applications. Importantly, we provide a constructive discussion on each category regarding its potential and challenges. Moreover, we review some crucial aspects of deep HDR imaging, such as datasets and evaluation metrics. Finally, we highlight some open problems and point out future research directions.

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Section: Hdr Imaging With the Attention Mechanismmentioning

confidence: 99%

Section: Hdr Imaging With the Attention Mechanismmentioning

confidence: 99%

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

Wang

Yoon

2021

Preprint

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“…Since high dynamic range (HDR) imaging is able to capture the wide range of real-world lighting, the HDR image meets the high requirements for image quality and visual perception [1][2][3]. At present, the HDR image can be found in many applications, such as computer vision, broadcasting, surveillance, Internet of things (IOT), and so on [4,5]. However, during the transmission process, the HDR image may be copied and stolen by unauthorized users, and their copyright needs to be protected.…”

Section: Introductionmentioning

confidence: 99%

A Novel Zero Watermarking Based on DT-CWT and Quaternion for HDR Image

Huang

Shi

et al. 2021

Electronics

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This paper presents a high dynamic range (HDR) image zero watermarking method based on dual tree complex wavelet transform (DT-CWT) and quaternion. In order to be against tone mapping (TM), DT-CWT is used to transform the three RGB color channels of the HDR image for obtaining the low-pass sub-bands, respectively, since DT-CWT can extract the contour of the HDR image and the contour change of the HDR image is small after TM. The HDR image provides a wide dynamic range, and thus, three-color channel correlations are higher than inner-relationships and the quaternion is used to consider three color channels as a whole to be transformed. Quaternion fast Fourier transform (QFFT) and quaternion singular value decomposition (QSVD) are utilized to decompose the HDR image for obtaining robust features, which is fused with a binary watermark to generate a zero watermark for copyright protection. Furthermore, the binary watermark is scrambled for the security by using the Arnold transform. Experimental results denote that the proposed zero-watermarking method is robust to TM and other image processing attacks, and can protect the HDR image efficiently.

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“…Traditional low dynamic range (LDR) images have a very limited ability to record the wide dynamic range, which causes that the light area is overexposed and the dark area is underexposed in the real scene [1,2]. Compared with the LDR imaging, the high dynamic range (HDR) imaging technology emerges in order to describe the real scene more accurately and record more detailed information in the light and dark areas [3,4]. As HDR images become more and more common in practical applications [5,6], the copyright protection of the HDR image has received increasing attention.…”

Section: Introductionmentioning

confidence: 99%

A Novel HDR Image Zero-Watermarking Based on Shift-Invariant Shearlet Transform

Shi

Luo

Huang

et al. 2021

Security and Communication Networks

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In this paper, a novel high dynamic range (HDR) image zero-watermarking algorithm against the tone mapping attack is proposed. In order to extract stable and invariant features for robust zero-watermarking, the shift-invariant shearlet transform (SIST) is used to transform the HDR image. Firstly, the HDR image is converted to CIELAB color space, and the L component is selected to perform SIST for obtaining the low-frequency subband containing the robust structure information of the image. Secondly, the low-frequency subband is divided into nonoverlapping blocks, which are transformed by using discrete cosine transform (DCT) and singular value decomposition (SVD) to obtain the maximum singular values for constructing a binary feature image. To increase the watermarking security, a hybrid chaotic mapping (HCM) is employed to get the scrambled watermark. Finally, an exclusive-or operation is performed between the binary feature image and the scrambled watermark to compute robust zero-watermark. Experimental results show that the proposed algorithm has a good capability of resisting tone mapping and other image processing attacks.

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Multi-exposure high dynamic range imaging with informative content enhanced network

Cited by 24 publications

References 17 publications

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

A Novel Zero Watermarking Based on DT-CWT and Quaternion for HDR Image

A Novel HDR Image Zero-Watermarking Based on Shift-Invariant Shearlet Transform

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