Self-Aligned Concave Curve

Wang, Wenjing; Xu, Zhengbo; Huang, Haofeng; Liu, Jiaying

doi:10.1145/3503161.3547991

Cited by 12 publications

(2 citation statements)

References 32 publications

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“…With the advent of deep learning, learning-based LLIE methods have emerged (Wang et al 2022b;Huang et al 2022). A series of supervised methods, such as the LL-Net (Lore, Akintayo, and Sarkar 2017), MBLLEN (Lv et al 2018), LPNet (Li et al 2021), and etc.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Neural Radiance Fields with Adaptive Multi-Exposure Fusion: A Bilevel Optimization Approach for Novel View Synthesis

Zou,

Li,

Jiang

et al. 2024

AAAI

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Neural Radiance Fields (NeRF) have made significant strides in the modeling and rendering of 3D scenes. However, due to the complexity of luminance information, existing NeRF methods often struggle to produce satisfactory renderings when dealing with high and low exposure images. To address this issue, we propose an innovative approach capable of effectively modeling and rendering images under multiple exposure conditions. Our method adaptively learns the characteristics of images under different exposure conditions through an unsupervised evaluator-simulator structure for HDR (High Dynamic Range) fusion. This approach enhances NeRF's comprehension and handling of light variations, leading to the generation of images with appropriate brightness. Simultaneously, we present a bilevel optimization method tailored for novel view synthesis, aiming to harmonize the luminance information of input images while preserving their structural and content consistency. This approach facilitates the concurrent optimization of multi-exposure correction and novel view synthesis, in an unsupervised manner. Through comprehensive experiments conducted on the LOM and LOL datasets, our approach surpasses existing methods, markedly enhancing the task of novel view synthesis for multi-exposure environments and attaining state-of-the-art results. The source code can be found at https://github.com/Archer-204/AME-NeRF.

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Section: Related Workmentioning

confidence: 99%

Enhancing Neural Radiance Fields with Adaptive Multi-Exposure Fusion: A Bilevel Optimization Approach for Novel View Synthesis

Zou,

Li,

Jiang

et al. 2024

AAAI

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show abstract

“…SCT [22] is trained in a task-inspired manner to be used in object tracking. Wenjing Wang et al [23] propose the use of discrete integration to learn illumination-enhanced concave curves that can be used for advanced vision tasks.…”

Section: Related Workmentioning

confidence: 99%

Low-Light Enhancer for UAV Night Tracking Based on Zero-DCE++

Zhang¹,

Li²,

Qin³

2023

JCC

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Unmanned aerial vehicle (UAV) target tracking tasks can currently be successfully completed in daytime situations with enough lighting, but they are unable to do so in nighttime scenes with inadequate lighting, poor contrast, and low signal-to-noise ratio. This letter presents an enhanced low-light enhancer for UAV nighttime tracking based on Zero-DCE++ due to its advantages of low processing cost and quick inference. We developed a light-weight UCBAM capable of integrating channel information and spatial features and offered a fully considered curve projection model in light of the low signalto-noise ratio of night scenes. This method significantly improved the tracking performance of the UAV tracker in night situations when tested on the public UAVDark135 and compared to other cutting-edge low-light enhancers. By applying our work to different trackers, this search shows how broadly applicable it is.

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Watching it in Dark: A Target-Aware Representation Learning Framework for High-Level Vision Tasks in Low Illumination

Li,

Zhang,

et al. 2024

Lecture Notes in Computer Science

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Self-Aligned Concave Curve

Cited by 12 publications

References 32 publications

Enhancing Neural Radiance Fields with Adaptive Multi-Exposure Fusion: A Bilevel Optimization Approach for Novel View Synthesis

Enhancing Neural Radiance Fields with Adaptive Multi-Exposure Fusion: A Bilevel Optimization Approach for Novel View Synthesis

Low-Light Enhancer for UAV Night Tracking Based on Zero-DCE++

Watching it in Dark: A Target-Aware Representation Learning Framework for High-Level Vision Tasks in Low Illumination

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