Color Constancy via Multi-Scale Region-Weighed Network Guided by Semantics

Wang, Fei; Wang, Wei; Wu, Dan; Gao, Guanbin

doi:10.3389/fnbot.2022.841426

Cited by 7 publications

(2 citation statements)

References 57 publications

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“…A quantitative comparison of the seven fusion results was chosen to avoid the interference of subjective human factors [ 35 , 36 , 37 ]. All fused images are based on publicly available code, and all data in the tables are from our own measurements of the fused images.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Infrared and Visible Image Fusion Method Using Salience Detection and Convolutional Neural Network

Wang

et al. 2022

Sensors

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This paper presents an algorithm for infrared and visible image fusion using significance detection and Convolutional Neural Networks with the aim of integrating discriminatory features and improving the overall quality of visual perception. Firstly, a global contrast-based significance detection algorithm is applied to the infrared image, so that salient features can be extracted, highlighting high brightness values and suppressing low brightness values and image noise. Secondly, a special loss function is designed for infrared images to guide the extraction and reconstruction of features in the network, based on the principle of salience detection, while the more mainstream gradient loss is used as the loss function for visible images in the network. Afterwards, a modified residual network is applied to complete the extraction of features and image reconstruction. Extensive qualitative and quantitative experiments have shown that fused images are sharper and contain more information about the scene, and the fused results look more like high-quality visible images. The generalization experiments also demonstrate that the proposed model has the ability to generalize well, independent of the limitations of the sensor. Overall, the algorithm proposed in this paper performs better compared to other state-of-the-art methods.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Infrared and Visible Image Fusion Method Using Salience Detection and Convolutional Neural Network

Wang

et al. 2022

Sensors

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“…In recent years, deep learning-based AWB algorithms 4 , 11 – 15 have made significant improvements to address this issue. For example, FC4 11 can generate final illuminant estimation results by establishing confidence maps for each color patch in the scene.…”

Section: Introductionmentioning

confidence: 99%

ReCC-GAN: scene global illuminant estimation network based on Retinex and generative adversarial network

Zhao,

Su,

Chai

et al. 2024

J. Electron. Imag.

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In capturing images, cameras often encounter color-cast problems owing to variations in the illumination of different scenes. Existing algorithms attempt to estimate the illuminant information from the raw image data captured by a camera sensor to address color-casting problems. However, these algorithms tend to overlook the potential interference of scene brightness information present in the raw data. Therefore, we introduce a novel illuminant estimation architecture, Retinex color constancy generative adversarial network (ReCC-GAN). ReCC-GAN aims to mitigate the impact of scene brightness information on illuminant estimation tasks, enhance global illuminant estimation accuracy, and resolve color-cast problems. The ReCC-GAN architecture initially separates the reflectance component from the raw data using the Retinex module, reducing the interference caused by the scene brightness information. Subsequently, a specially designed GAN for illuminant estimation was employed to estimate the illuminant vector from this component more effectively. This approach improved illumination estimation accuracy. Experimental results on the Cube++ and NUS-8 dataset demonstrated that the proposed algorithm achieves competitive results compared with other solutions, with outstanding performance on the worst 25% metric. Additionally, tests on various datasets confirmed that the ReCC-GAN exhibited strong generalization performance, further validating its effectiveness and rationality in scene-wide illuminant estimation tasks.

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Multi-scale color constancy based on salient varying local spatial statistics

Ulucan,

Ebner

2023

Vis Comput

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The human visual system unconsciously determines the color of the objects by “discounting” the effects of the illumination, whereas machine vision systems have difficulty performing this task. Color constancy algorithms assist computer vision pipelines by removing the effects of the illuminant, which in the end enables these pipelines to perform better on high-level vision tasks based on the color features of the scene. Due to its benefits, numerous color constancy algorithms have been developed, and existing techniques have been improved. Combining different strategies and investigating new methods might help us design simple yet effective algorithms. Thereupon, we present a color constancy algorithm based on the outcomes of our previous works. Our algorithm is built upon the biological findings that the human visual system might be discounting the illuminant based on the highest luminance patches and space-average color. We find the illuminant estimate based on the idea that if the world is gray on average, the deviation of the brightest pixels from the achromatic value should be caused by the illuminant. Our approach utilizes multi-scale operations by only considering the salient pixels. It relies on varying surface orientations by adopting a block-based approach. We show that our strategy outperforms learning-free algorithms and provides competitive results compared to the learning-based methods. Moreover, we demonstrate that using parts of our strategy can significantly improve the performance of several learning-free methods. We also briefly present an approach to transform our global color constancy method into a multi-illuminant color constancy approach.

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Color Constancy via Multi-Scale Region-Weighed Network Guided by Semantics

Cited by 7 publications

References 57 publications

Infrared and Visible Image Fusion Method Using Salience Detection and Convolutional Neural Network

Infrared and Visible Image Fusion Method Using Salience Detection and Convolutional Neural Network

ReCC-GAN: scene global illuminant estimation network based on Retinex and generative adversarial network

Multi-scale color constancy based on salient varying local spatial statistics

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