Fast polarimetric dehazing method for visibility enhancement in HSI colour space

Zhang, Wenfei; Liang, Jian; Ren, Liyong; Ju, Haijuan; Bai, Zhaofeng; Wu, Zhaoxin

doi:10.1088/2040-8986/aa7f39

Cited by 15 publications

(34 citation statements)

References 26 publications

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“…The contrast of Figure 4e is 0.3748, which is much less than the proposed method. Moreover, the dehazing capability of the previous proposed method with other dehazing methods has been fully compared in [27], so in the following, we mainly focus on the effectiveness and robustness of the proposed method with the previous method in the discussion.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Generalized Polarimetric Dehazing Method Based on Low-Pass Filtering in Frequency Domain

Liang

Ren

et al. 2020

Sensors

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Polarimetric dehazing methods can significantly enhance the quality of hazy images. However, current methods are not robust enough under different imaging conditions. In this paper, we propose a generalized polarimetric dehazing method based on low-pass filtering in the frequency domain. This method can accurately estimate the polarized state of the scattering light automatically without adjusting bias parameters. Experimental results show the effectiveness and robustness of our proposed method in different hazy weather and scattering underwater environments with different densities. Furthermore, computational efficiency is enhanced more than 70% compared to the polarimetric dehazing method we proposed previously.

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Section: Experiments and Discussionmentioning

confidence: 99%

Generalized Polarimetric Dehazing Method Based on Low-Pass Filtering in Frequency Domain

Liang

Ren

et al. 2020

Sensors

Self Cite

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“…Although the formulas for the HSI2RGB conversion, as shown in (4)- (8), are determined by the position of the H component, regardless of the sector in which the H component is located, the cosine term must be calculated. If the cosine term cannot be optimized, this part would cost a large amount of logic resources and cause many difficulties in the process of timing sequence optimization during FPGA implementation.…”

Section: ) the Conversion From The Hsi Color Space To The Rgb Color mentioning

confidence: 99%

“…According to 2) in part A, the formulas for the HSI2RGB conversion are determined by the position of the H component. However, according to (6)- (8), utilizing ′ H to replace H as the hue component would cause the loss of sector information. In other words, with the H component changing in the range [0,2 ) π , the ′ H component would lose the sector information.…”

Section: B the Optimization Of The Rgb-hsi Color Space Conversion Inmentioning

confidence: 99%

“…Due to the reasons mentioned above, researchers have done much work on visible light image processing based on the HSI color space. A fast haze removal method based on the polarimetric imaging technique by combining the HSI color space and retinex theory was proposed in [8]. The method in this reference could recover the information data in an image with a fast process.…”

Section: Introductionmentioning

confidence: 99%

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An FPGA-Based Simple RGB-HSI Space Conversion Algorithm for Hardware Image Processing

et al. 2020

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In this paper, a new, low-complexity, easy-to-implement hardware method for color space conversion between the red-green-blue (RGB) and the hue-saturation-intensity (HSI) color spaces called the simple RGB-HSI space conversion (S-SC) algorithm is proposed, which aims to provide more rapid computing due to the need for fewer operations. In the S-SC algorithm, we reconstruct the model of space conversion between the RGB color space and the HSI color space (RGB-HSI) by inverting the conversion from the HSI color space to the RGB color space of the traditional geometric derivation algorithm. As a result, the nonlinear model-realized RGB-HSI color space conversion by the geometric derivation algorithm is transformed into a linear conversion model, which can avoid complicated calculations such as trigonometric and inverse trigonometric functions in the color space conversion process. The model can effectively reduce the computational complexity of the algorithm and facilitate hardware implementation at the same time. To evaluate the performance of the S-SC algorithm, we first compare the S-SC algorithm with the geometric derivation algorithm from the computational complexity perspective. On this basis, we compare the S-SC algorithm with five other RGB-HSI color space conversion algorithms from the perspectives of error and conversion effect. Finally, we use the field programmable gate array (FPGA) hardware platform to analyze and verify the timing sequence and logical resource consumption and verify the effectiveness of the proposed algorithm with experimental results. We show that the S-SC algorithm achieves good performance in terms of conversion accuracy, logical unit resource occupancy, and output timing.

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“…In general, color distortion is occurred if the ratio of the three components change. HSI model uses hue ( H ), saturation ( S ) and intensity ( I ) to represent color images and it is compatible with human visual system as well [ 18 ]. The HSI model representation is shown in Figure 2 .…”

Section: Related Workmentioning

confidence: 99%

A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

et al. 2018

Sensors

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Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.

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Fast polarimetric dehazing method for visibility enhancement in HSI colour space

Cited by 15 publications

References 26 publications

Generalized Polarimetric Dehazing Method Based on Low-Pass Filtering in Frequency Domain

Generalized Polarimetric Dehazing Method Based on Low-Pass Filtering in Frequency Domain

An FPGA-Based Simple RGB-HSI Space Conversion Algorithm for Hardware Image Processing

A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

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