High-precision calibration of binocular camera with super-resolution technology

doi:10.23919/JSEE.2026.000105

Abstract

Abstract:

The calibration of parameters for onboard stereo cameras is crucial for achieving efficient visual-assisted driving. However, in practical scenarios, low-resolution images often result in inaccuracies in feature point extraction, thereby affecting the accuracy of camera parameter calibration. To address this issue, this paper proposes a self-calibration method for stereo cameras based on joint de-noising, de-mosaic, de-ringing, and super-resolution network (JDDDSN) super-resolution reconstruction. By reconstructing images into higher-resolution images with richer details, feature points are extracted for extrinsic calibration of stereo cameras. For real-world driving scenarios, the reconstructed images achieve noise and ringing artifact reduction while obtaining clearer high-resolution images. This study further investigates the impact of the super-resolution reconstruction network on target area calibration at various distances. Additionally, it highlights the significant role of super-resolution in enhancing stereo camera calibration accuracy by removing dynamic points and focusing on static regions. Through a series of experiments, this paper validates the effectiveness and practicality of the JDDDSN super-resolution reconstruction network in improving stereo camera calibration accuracy, demonstrating its application value in the field of stereoscopic vision.

Key words: super-resolution reconstruction, camera calibration, self-calibration, binocular camera

Ting SUN, Chao MA, Tian SUN, Shanshan PEI, Qian LONG. High-precision calibration of binocular camera with super-resolution technology[J]. Journal of Systems Engineering and Electronics, 2026, 37(3): 844-860.

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Name	Specific parameter
Resolution	1 280×720 pixels
Baseline/mm	120
(c_x, c_y)	(649.5382, 340.9208) pixels
Focal length of lens/mm	8
Dynamic range/dB	120
Processor	FPGA, Dual-Core ARM
Support interface	CAN, RS485, network port
Dimensions/cm³	25×15×8

Sampling rate	Method	Set5(PSNR/dB)/(SSIM)	Set14(PSNR/dB)/(SSIM)	BSD100(PSNR/dB)/(SSIM)	Urban100(PSNR/dB)/(SSIM)
2×	Bicubic	33.66/0.929	30.24/0.869	29.56/0.843	26.88/0.840
	SRCNN	36.66/0.954	33.45/0.907	31.36/0.888	29.50/0.895
	SRGAN	−	−	30.48/0.863	29.40/0.847
	VDSR	37.53/0.959	33.03/0.912	31.90/0.896	30.76/0.914
	JDDDSN	37.88/0.960	33.65/0.923	32.01/0.902	31.12/0.931
3×	Bicubic	30.39/0.868	27.55/0.774	27.21/0.739	24.46/0.735
	SRCNN	32.75/0.909	29.30/0.822	28.41/0.786	26.24/0.799
	SRGAN	30.87/0.896	28.03/0.781	26.90/0.710	25.23/0.759
	VDSR	33.66/0.921	29.77/0.831	28.83/0.797	27.14/0.827
	JDDDSN	33.64/0.916	29.81/0.827	28.79/0.791	27.56/0.843
4×	Bicubic	28.42/0.810	26.00/0.703	25.96/0.668	23.14/0.658
	SRCNN	30.48/0.863	27.50/0.751	26.90/0.710	24.52/0.722
	SRGAN	29.40/0.847	26.02/0.739	25.16/0.668	24.38/0.737
	VDSR	31.35/0.883	28.01/0.768	27.29/0.725	25.18/0.752
	JDDDSN	30.99/0.871	28.11/0.771	27.70/0.741	25.89/0.786

Image	Scene 1	Scene 2	Scene 3	Scene 4	Mean
Original image	0.1611	0.1275	0.1305	0.1426	0.1404
VDSR image	0.1606	0.1277	0.1268	0.1352	0.1375
JDDDSN image	0.1492	0.1276	0.1276	0.1048	0.1273

Image	Distance/m
Image	0−10	10−20	20−30	30−40	40−50	50−60	60−70	70−80	80−90	90−100
Original image	0.0124	0.1033	0.1170	0.1174	0.1086	0.0719	0.0759	0.3347	0.3333	0.5738
SR image	0.0113	0.0382	0.1154	0.0630	0.0845	0.0701	0.0650	0.1127	0.2988	0.2038

Image	Scene 1	Scene 2	Scene 3	Scene 4	Mean
Original image	0.1367	0.1206	0.1138	0.1108	0.1237
VDSR image	0.1273	0.1183	0.1130	0.0492	0.1019
JDDDSN image	0.1238	0.1194	0.1110	0.0427	0.0992