Near-Infrared Fusion via Color Regularization for Haze and Color Distortion Removals

Son, Chang-Hwan; Zhang, Xiaoping

doi:10.1109/tcsvt.2017.2748150

Cited by 50 publications

(20 citation statements)

References 26 publications

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“…11 Journal of Sensors et al [20], Schaul et al [21], Son and Zhang et al [22], and Li and Wu [23] are selected for comparison. For the visible light-near infrared fusion defogging algorithm based on deep learning, the results of Li et al [26], Liu et al [27], and Li et al [28] are used for comparison.…”

Section: Resultsmentioning

confidence: 99%

“…From the perspective of image source, image defogging can be divided into multi-image defogging and single image defogging. The defogging effect could be finally achieved by using multiple images, such as polarized images [15,16], sunny images [17], image frame sequences [18], and infrared images [19][20][21][22] as the auxiliary images. Such algorithms are complex to realize but with good restoration effects.…”

Section: Introductionmentioning

confidence: 99%

“…Schaul et al [21] achieved the fusion of near-infrared images and color visible light images by using a multiresolution method of edgepreserving filters, which could minimize the artifacts and achieve the purpose of defogging. Son and Zhang [22] raised a near-infrared fusion model combining new color and depth regularization with the traditional fog degradation model and proposed the color regularization method based on the combination of color near-infrared image and the captured visible image. The color range of the unknown fogless image is set so that the continuously estimated depth map would not deviate greatly, thereby the natural color of the image could be transmitted and the visibility of the image can be improved.…”

Section: Introductionmentioning

confidence: 99%

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Defogging Technology Based on Dual-Channel Sensor Information Fusion of Near-Infrared and Visible Light

Yuan

Peng

et al. 2020

Journal of Sensors

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Since the method to remove fog from images is complicated and detail loss and color distortion could occur to the defogged images, a defogging method based on near-infrared and visible image fusion is put forward in this paper. The algorithm in this paper uses the near-infrared image with rich details as a new data source and adopts the image fusion method to obtain a defog image with rich details and high color recovery. First, the colorful visible image is converted into HSI color space to obtain an intensity channel image, color channel image, and saturation channel image. The intensity channel image is fused with a near-infrared image and defogged, and then it is decomposed by Nonsubsampled Shearlet Transform. The obtained high-frequency coefficient is filtered by preserving the edge with a double exponential edge smoothing filter, while low-frequency antisharpening masking treatment is conducted on the low-frequency coefficient. The new intensity channel image could be obtained based on the fusion rule and by reciprocal transformation. Then, in color treatment of the visible image, the degradation model of the saturation image is established, which estimates the parameters based on the principle of dark primary color to obtain the estimated saturation image. Finally, the new intensity channel image, the estimated saturation image, and the primary color image are reflected to RGB space to obtain the fusion image, which is enhanced by color and sharpness correction. In order to prove the effectiveness of the algorithm, the dense fog image and the thin fog image are compared with the popular single image defogging and multiple image defogging algorithms and the visible light-near infrared fusion defogging algorithm based on deep learning. The experimental results show that the proposed algorithm is better in improving the edge contrast and the visual sharpness of the image than the existing high-efficiency defogging method.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Defogging Technology Based on Dual-Channel Sensor Information Fusion of Near-Infrared and Visible Light

Yuan

Peng

et al. 2020

Journal of Sensors

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“…Thanks to this property, NIR images are usually used in nighttime for object detection systems [1]- [3] or human assistant systems [4]. NIR images are also used as an important cue for RGB color correction, detail enhancement and haze removal by fusion [5]- [13]. However, NIR images are of one channel (no color information), and are much different from human visual perception.…”

Section: Introductionmentioning

confidence: 99%

NIR to RGB Domain Translation Using Asymmetric Cycle Generative Adversarial Networks

Sun

Jung

et al. 2019

IEEE Access

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Near infrared (NIR) images have clear textures but do not contain color. In this paper, we propose NIR to RGB domain translation using asymmetric cycle generative adversarial networks (ACGANs). The RGB image (3 channels) has richer information than the NIR image (1 channel), which makes NIR-RGB domain translation asymmetric in information. We adopt asymmetric cycle GANs that have different network capacities according to the translation direction. We combine UNet and ResNet in generator and use the feature pyramid networks (FPNs) in discriminator. With the help of a 128 × 128 large receptive field, we capture rich spatial context information with a multiscale architecture. Experimental results show that the proposed method achieves natural looking NIR colorization results with high generalization ability, i.e. feasible in category unaware cases, and outperforms state-of-the-art ones in realistic colorization and resistance to unregistration.

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“…They enhance contrast and saturability, but degrade visibility and lose depth information. Then, addition-information based methods [5], [6] and multiple-images methods [7]- [9] have been proposed. They are not always available in practice because of the high cost to obtain additional information or images.…”

Section: Introductionmentioning

confidence: 99%

Hardware Implementation for Haze Removal With Adaptive Filtering

Zhang

Wei

2019

IEEE Access

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Haze removal is useful in computational photography and computer vision applications. Single image haze removal is still a challenge for real-time embedded systems. In general, dark channel prior approach can effectively remove haze from a single image. However, the halo effect is the major problem of dehazing. In this study, adaptive filtering, which is easy for VLSI design, is introduced to refine the transmission map and improve the result of haze removal. A hardware architecture for our haze removal method is proposed to achieve the real-time requirement. A specific hardware component is designed to compute lengths of the paths and adaptive structuring element (ASE). The hardware architecture for haze removal is implemented in 180 nm CMOS technology and occupies 5.46 mm 2 with 143 K bit on-chip memory. The design can operate at 177MHz and support for HD (1280×720) 192 frame/s. Experiment results show that the hardware implementation is suitable in embedded system for real-time HD applications.

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Near-Infrared Fusion via Color Regularization for Haze and Color Distortion Removals

Cited by 50 publications

References 26 publications

Defogging Technology Based on Dual-Channel Sensor Information Fusion of Near-Infrared and Visible Light

Defogging Technology Based on Dual-Channel Sensor Information Fusion of Near-Infrared and Visible Light

NIR to RGB Domain Translation Using Asymmetric Cycle Generative Adversarial Networks

Hardware Implementation for Haze Removal With Adaptive Filtering

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