ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

Liu, Zhen; Lin, Wenjie; Li, Xinpeng; Rao, Qing; Jiang, Tuo; Han, Mingyan; Fan, Haoqiang; Sun, Jian; Liu, Shuaicheng

doi:10.48550/arxiv.2105.10697

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…The methods in the first group [19,23,24,26,29,30,31,32,33,34,35,36,37,38] suffer from the problem of not accurately processing over-and under-exposed regions, but provide more compact systems. Methods in the second group use a group of bracketed overand under-exposed images as input to directly learn to generate HDR output [39,39,40,41,42,43,44,45,46], as similarly utilized in photographic HDR generation. Methods belonging to these two groups differ mainly according to their network components, such as non-local blocks [42] and attention mechanisms [40].…”

Section: Related Workmentioning

confidence: 99%

A Mixed Quantization Network for Computationally Efficient Mobile Inverse Tone Mapping

Borrego-Carazo¹,

Özay²,

Laboyrie³

et al. 2022

Preprint

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Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) image, namely inverse tone mapping (ITM), is challenging due to the lack of information in over-and under-exposed regions. Current methods focus exclusively on training high performing but computationally inefficient ITM models, which in turn hinder deployment of the ITM models in resource-constrained environments with limited computing power such as edge and mobile device applications.To this end, we propose combining efficient operations of deep neural networks with a novel mixed quantization scheme to construct a well performing but computationally efficient mixed quantization network (MQN) which can perform single image ITM on mobile platforms. In the ablation studies, we explore the effect of using different attention mechanisms, quantization schemes and loss functions on the performance of MQN in ITM tasks. In the comparative analyses, ITM models trained using MQN perform on par with the state-of-the-art methods on benchmark datasets. MQN models provide up to 10 times improvement on latency and 25 times improvement on memory consumption.

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Section: Related Workmentioning

confidence: 99%

A Mixed Quantization Network for Computationally Efficient Mobile Inverse Tone Mapping

Borrego-Carazo¹,

Özay²,

Laboyrie³

et al. 2022

Preprint

View full text Add to dashboard Cite

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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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ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

Cited by 2 publications

References 38 publications

A Mixed Quantization Network for Computationally Efficient Mobile Inverse Tone Mapping

A Mixed Quantization Network for Computationally Efficient Mobile Inverse Tone Mapping

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

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