Bionic orientation method based on polarization imaging in HDR scenes

Xuesong Wu; Chen Fan; Xiaofeng He; Lilian Zhang; Xiaoping Hu; Ying Fan; Guoliang Han; Wenzhou Zhou; Hang Shang

doi:10.1364/AO.448701

Applied Optics
Vol. 61,
Issue 8,
pp. 2007-2018
(2022)
•https://doi.org/10.1364/AO.448701

Bionic orientation method based on polarization imaging in HDR scenes

Xuesong Wu, Chen Fan, Xiaofeng He, Lilian Zhang, Xiaoping Hu, Ying Fan, Guoliang Han, Wenzhou Zhou, and Hang Shang

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Xuesong Wu, Chen Fan, Xiaofeng He, Lilian Zhang, Xiaoping Hu, Ying Fan, Guoliang Han, Wenzhou Zhou, and Hang Shang, "Bionic orientation method based on polarization imaging in HDR scenes," Appl. Opt. 61, 2007-2018 (2022)

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Table of Contents Category
- Imaging Systems, Image Processing, and Displays
Optics & Photonics Topics
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The topics in this list come from the Optics and Photonics Topics applied to this article.

About this Article
History
- Original Manuscript: November 19, 2021
- Revised Manuscript: January 28, 2022
- Manuscript Accepted: February 2, 2022
- Published: March 4, 2022

Abstract

An increasing number of bio-inspired navigation approaches have been designed based on polarization cameras. However, digital cameras can sense a much narrower field of vision than the vision of insects or human beings. In this study, we propose an adaptive skylight polarized orientation method for high dynamic range (HDR) scenes. Initially, we built a model of the image acquisition pipeline that can recover HDR irradiance maps from polarization images. Subsequently, the orientation method was designed based on a combination of the irradiance maps and the least squares methods. Some preprocessing steps were utilized to eliminate occlusion interference. In addition, an autoexposure adjustment method was proposed using information entropy and heuristic segmentation. Finally, the experimental results show that the proposed method can improve the accuracy of bionic orientation and adaption to skylight with occlusions and interference in natural conditions.

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Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Radiometry	Units
Scene Radiance ( $S$ )	Watts per steradian per square meter, $W \times s r^{- 1} \times m^{- 2}$
Image Irradiance ( $I_{0}$ )	Watts per square meters, $W \times m^{- 2}$
Polarized Irradiance ( $I (k)$ , $k = 1 \sim 4$ )	Watts per square meters, $W \times m^{- 2}$
Pixel Value ( $L (k)$ , $k = 1 \sim 4$ )	Joules, $J$

AOP Calculation	Mean Error (deg)	Std (deg)
HDR	0.40	0.15
Autoexposure	2.29	3.84

AOP Calculation	MAE (deg)	RMSE (deg)
HDR	0.30	0.36
Manual Setting	0.44	0.53
Autoexposure	–	–

Weather Condition	Autoexposure	Underexposure	Optical Manual Setting	HDR
Partly Cloudy	6000	1000	2000	[500, 1000, 2000, 4500, 6000, 10000]
Cloudy	15000	3000	7500	[750, 1500, 3000, 7500, 15000, 30000]

Method	MAE	RMSE
Wang $+$ underexposure	1.32	1.73
Wang $+$ auto	–	–
Wang $+$ optimal	0.51	0.64
Wang $+$ POL $-$ HDR	0.26	0.36
Line $+$ underexposure	1.69	2.08
Line $+$ auto	–	–
Line $+$ optimal	0.64	0.84
Line $+$ POL $-$ HDR	0.53	0.59

Method	MAE	RMSE
Wang $+$ underexposure	2.58	3.36
Wang $+$ auto	–	–
Wang $+$ optimal	0.90	1.35
Wang $+$ POL $-$ HDR	0.54	0.70
Line $+$ underexposure	3.28	4.37
Line $+$ auto	–	–
Line $+$ optimal	0.98	1.31
Line $+$ POL − HDR	0.75	0.96

Exposure Time (µs)	Global Entropy	Local Entropy	Wang (MAE)	Wang (RMSE)	Line (MAE)	Line (RMSE)
500	3.77	4.56	4.96	5.89	–	–
1000	4.70	5.73	1.32	1.73	1.69	2.08
2000	5.66	6.31	0.51	0.64	0.64	0.84
4500	6.49	4.88	4.28	4.92	–	–
6000	6.63	1.20	–	–	–	–
10000	6.98	–	–	–	–	–

Exposure Time (µs)	Global Entropy	Local Entropy	Wang (MAE)	Wang (RMSE)	Line (MAE)	Line (RMSE)
750	2.42	2.84	–	–	–	–
1500	3.34	4.90	5.63	7.33	8.70	11.52
3000	4.25	5.46	2.58	3.36	3.28	4.37
7500	5.52	6.55	0.90	1.35	0.98	1.31
15000	6.29	6.04	1.61	1.97	2.13	2.79
30000	–	–	–	–	–	–

S-RANSAC Algorithm
1. $E^{i n l i e r s} = []$ , $n = 10000$ , $m =$ 1
2. While $m \leq n$ , do
3. Select two uncorrelated vectors $E_{m} = [e_{i}, e_{j}]$ randomly from $E$
4. Estimate $s$ using Eq. (18)
5. Find inliers $E_{m}^{i n l i e r s}$ based on $E^{T} s = 0$
6. If size ( $E_{m}^{i n l i e r s}$ ) > size( $E^{i n l i e r s}$ )
$E^{i n l i e r s} = E_{m}^{i n l i e r s}$ , $ς = \frac{s i z e (E^{i n l i e r s})}{s i z e (E)}$
$n = \frac{\log (1 - P)}{\log (1 - ς^{2})}$
7. End if
8. End while

Exposure Control Algorithm for Orientation
1. Calculate the entropy of the autoexposure image: Entropy (Image)
2. While Entropy (Image) < threshold, do
Take the exposure stack, the exposure setting is $[\frac{1}{2^{n - 2}} * t,$
$\dots \frac{1}{2^{2}} * t, \frac{1}{2} * t, t, 2 * t]$ , where $t$ is the initial exposure time.
3. Calculate the DOP based on the image with the highest
entropy (or the HDR image).
4. Perform segmentation and morphological filtering with the
DOP information.
5. Calculate the entropy in the segmentation area, and select the
exposure time corresponding to the image with the largest
value.
End while

S-RANSAC Algorithm
1. $E^{i n l i e r s} = []$ , $n = 10000$ , $m =$ 1
2. While $m \leq n$ , do
3. Select two uncorrelated vectors $E_{m} = [e_{i}, e_{j}]$ randomly from $E$
4. Estimate $s$ using Eq. (18)
5. Find inliers $E_{m}^{i n l i e r s}$ based on $E^{T} s = 0$
6. If size ( $E_{m}^{i n l i e r s}$ ) > size( $E^{i n l i e r s}$ )
$E^{i n l i e r s} = E_{m}^{i n l i e r s}$ , $ς = \frac{s i z e (E^{i n l i e r s})}{s i z e (E)}$
$n = \frac{\log (1 - P)}{\log (1 - ς^{2})}$
7. End if
8. End while

Exposure Control Algorithm for Orientation
1. Calculate the entropy of the autoexposure image: Entropy (Image)
2. While Entropy (Image) < threshold, do
Take the exposure stack, the exposure setting is $[\frac{1}{2^{n - 2}} * t,$
$\dots \frac{1}{2^{2}} * t, \frac{1}{2} * t, t, 2 * t]$ , where $t$ is the initial exposure time.
3. Calculate the DOP based on the image with the highest
entropy (or the HDR image).
4. Perform segmentation and morphological filtering with the
DOP information.
5. Calculate the entropy in the segmentation area, and select the
exposure time corresponding to the image with the largest
value.
End while

Abstract

Data availability

Cited By

Figures (13)

Tables (10)

Equations (22)

Applied Optics