Polarization-Based Dehazing Using Two Reference Objects

Miyazaki, Daisuke; Akiyama, Daisuke; Baba, Masashi; Furukawa, Ryo; Hiura, Shinsaku; Asada, Naoki

doi:10.1109/iccvw.2013.117

Cited by 23 publications

(12 citation statements)

References 10 publications

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“…These algorithms establish the atmospheric scattering model [7] [12]. The polarization images with different brightnesses of the same circumstance of the scene are obtained by using a polarization filter with different orientations.…”

Section: Image Restoration Methodsmentioning

confidence: 99%

A Review of Image Restoration based Image Defogging Algorithms

Bansal¹,

Sidhu²,

Jyoti³

2017

IJIGSP

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Abstract-Haze and fog lead to image degradation by various degradation processes like image contrast, image blurring and pixel distortion. It has effected the efficiency of computer and machine vision algorithms. A number of single image and multiple image restoration based image defogging algorithms have aimed to solve the problem in an efficient and fast manner. The objective of the paper is to summarize present state of the art image defogging algorithms. Firstly, an image classification algorithm has been presented and then we summarized present state of the art image restoration based image defogging algorithms. Finally, we summarized image quality assessment methods followed by their comparisons of various image defogging algorithms. Problems of image dehazing and future scope have been discussed thereafter.

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Section: Image Restoration Methodsmentioning

confidence: 99%

A Review of Image Restoration based Image Defogging Algorithms

Bansal¹,

Sidhu²,

Jyoti³

2017

IJIGSP

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show abstract

“…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%

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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“…In Zhao et al [3], polarization vision techniques are used for reconstructing 3D images and image dehazing. In [4], a polarization-based method is proposed as a method of enhancing the visibility of an image by canceling the haze effect. Miyazaki et al [5] proposed a method of estimating the surface normal of black specular objects through polarization analysis of the reflected light.…”

Section: Introductionmentioning

confidence: 99%

An Improved Calibration Technique for Polarization Images

et al. 2019

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Polarization imaging can be used to improve the image quality by reducing the effects of the unwanted light reflection, enhancing the quality of images taken in non-ideal conditions, such as foggy weather, or reconstructing the 3D form of an object based on the shape from polarization. Reconstructing a 3D form requires the acquisition of multiple images and inference of the polarization parameters, such as the degree of polarization and angle of polarization, from these experimental data. The light polarization extraction process should begin by performing the photometric calibration to improve the performance of the polarize instrument measurements by reducing the measurement errors and improving the consistency between measurements. We propose an algorithm based on the kernel ridge regression method to estimate both the polarization parameters and the measurement (angle) errors in the placement of the polarizer. The algorithm was tested on four different sets of images containing different objects based on their responses to light and polarization. The algorithm that uses the mixed kernel function gives the best results. INDEX TERMS Calibration, kernel ridge regression, polarization, polarizer.

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Polarization-Based Dehazing Using Two Reference Objects

Cited by 23 publications

References 10 publications

A Review of Image Restoration based Image Defogging Algorithms

A Review of Image Restoration based Image Defogging Algorithms

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

An Improved Calibration Technique for Polarization Images

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