Retinex-Based Relighting for Night Photography

Oishi, Sou; Fukushima, Norishige

doi:10.3390/app13031719

Cited by 8 publications

(7 citation statements)

References 54 publications

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“…where g x • denotes a decomposing operator, as described in Equations ( 11)- (13). Equation ( 14) summarizes the DMF formulation.…”

Section: Recursive Representation For Decomposed Multilateral Filteringmentioning

confidence: 99%

“…Sensor fusion can combine beneficial information from different sensors to generate a richer single image. Image signal fusion approaches have various applications: RGB and infrared image fusion [ 1 , 2 , 3 ], RGB and multispectral image fusion [ 4 ], intercolor RGB signal fusion [ 5 , 6 ], RGB and depth fusion [ 7 , 8 ], RGB and light fusion [ 9 ], RGB and computed edge fusion [ 10 ], different focal image fusion [ 11 , 12 ], CT and MRI signal fusion for medical image processing [ 3 ], Retinex-based enhancement [ 13 ], SAR and multispectral image fusion [ 14 ], and general signal fusion [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images

Nogami,

Kanetaka,

Naganawa

et al. 2024

Sensors

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This paper proposes an efficient algorithm for edge-preserving filtering with multiple guidance images, so-called multilateral filtering. Multimodal signal processing for sensor fusion is increasingly important in image sensing. Edge-preserving filtering is available for various sensor fusion applications, such as estimating scene properties and refining inverse-rendered images. The main application is joint edge-preserving filtering, which can preferably reflect the edge information of a guidance image from an additional sensor. The drawback of edge-preserving filtering lies in its long computational time; thus, many acceleration methods have been proposed. However, most accelerated filtering cannot handle multiple guidance information well, although the multiple guidance information provides us with various benefits. Therefore, we extend the efficient edge-preserving filters so that they can use additional multiple guidance images. Our algorithm, named decomposes multilateral filtering (DMF), can extend the efficient filtering methods to the multilateral filtering method, which decomposes the filter into a set of constant-time filtering. Experimental results show that our algorithm performs efficiently and is sufficient for various applications.

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“…where g x • denotes a decomposing operator, as described in Equations ( 11)- (13). Equation ( 14) summarizes the DMF formulation.…”

Section: Recursive Representation For Decomposed Multilateral Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images

Nogami,

Kanetaka,

Naganawa

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The retinex theory suggests that an image S(x,y) is composed of illumination component L(x,y) and reflection component R(x,y) 18 and its relationship is expressed in logarithmic form, as shown in Eq. (11).…”

Section: Design Of Demodulation Algorithmmentioning

confidence: 99%

Experimental research on image demodulation for optical camera communication

Ke,

Liu,

Liang

et al. 2023

Opt. Eng.

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“…Haze removal methods based on image enhancement primarily aim to enhance image contrast and highlight image details. These methods include the adaptive histogram equalization method [10], Retinex theory [11], etc. While these methods are simple, easy to implement, and widely applicable, they may lead to loss of details or overenhancement.…”

Section: Related Work 21 Image Haze Removalmentioning

confidence: 99%

A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning

Zhou

Zhao

Chen

et al. 2023

JMSE

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Obtaining ship navigation information from maritime videos can significantly improve maritime supervision efficiency and enable timely safety warnings. Ship detection and tracking are essential technologies for mining video information. However, current research focused on these advanced vision tasks in maritime supervision is not sufficiently comprehensive. Taking into account the application of ship detection and tracking technology, this study proposes a deep learning-based ship speed extraction framework under the haze environment. First, a lightweight convolutional neural network (CNN) is used to remove haze from images. Second, the YOLOv5 algorithm is used to detect ships in dehazed marine images, and a simple online and real-time tracking method with a Deep association metric (Deep SORT) is used to track ships. Then, the ship’s displacement in the images is calculated based on the ship’s trajectory. Finally, the speed of the ships is estimated by calculating the mapping relationship between the image space and real space. Experiments demonstrate that the method proposed in this study effectively reduces haze interference in maritime videos, thereby enhancing the image quality while extracting the ship’s speed. The mean squared error (MSE) for multiple scenes is 0.3 Kn on average. The stable extraction of ship speed from the video achieved in this study holds significant value in further ensuring the safety of ship navigation.

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Retinex-Based Relighting for Night Photography

Cited by 8 publications

References 54 publications

Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images

Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images

Experimental research on image demodulation for optical camera communication

A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning

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