(PDF) LUXFormer: Low-light image enhancement via joint spatial
Low-light image enhancement techniques have significantly progressed, but unstable image quality recovery and unsatisfactory visual perception are still significant challenges.
Get QuoteIn summary, the main contribution is three-fold: We propose an edge computing driven deep learning method for object detection in low-light conditions, which designs the overall structure of cloud-bas...
HOME / The Role of the Light Decay Enhancement Module - Indzawo Optic Connect
The Role of the Light Decay Enhancement Module - Indzawo Optic Connect [PDF]
Low-light image enhancement techniques have significantly progressed, but unstable image quality recovery and unsatisfactory visual perception are still significant challenges.
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This study presents a versatile Low-Light Enhancement Module (LLEM) to enhance object detection models, particularly for workpieces in challenging lighting cond
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Enhancing low-light images in construction scenarios is crucial for reliable structural health monitoring and automated defect detection in building projects.
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To address the issues of low model perception and decreased detection accuracy caused by low-light image characteristics, we designed a low-light enhancement module.
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In order to solve this problem, this paper proposed a low-illumination enhancement method based on structural and detail layers. Firstly, we designed an SRetinex-Net model. The
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This not only demonstrates the importance of introducing depth information for low-light image enhancement, but also validates the effectiveness of the MFC module we designed.
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Enhancing low-light images in construction scenarios is crucial for reliable structural health monitoring and automated defect detection in building
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We propose parallel and dynamic enhancement subnet-works for extremely low-light image enhancement in cloud computing stage, which not only saves computing time by parallel running
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Hence, this study aims to help improve video object detection, particularly in regions of low illumination, utilizing the Enhanced Zero DCE model. This deep-learning framework does not require any
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To address these challenges, we propose a bidirectional diffusion optimization mechanism that jointly models the degradation processes of both low-light and normal-light images,
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IAFE is explicitly designed for detection-oriented enhancement by jointly modeling unified light adaptation and extracting object-related features, thereby aligning the optimization objectives of
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Low-light image enhancement techniques have significantly progressed, but unstable image quality recovery and unsatisfactory visual
Get Quote