[1] Long Q, Xie Q W, Mita S et al. Real-time dense disparity estimation based on multi-path Viterbi for intelligent vehicle applications[C](2014).
[2] Long Q, Xie Q W, Mita S et al. A real-time dense stereo matching method for critical environment sensing in autonomous driving[C], 853-860(2014).
[3] Luo H G, Zhu L M, Ding H. Camera calibration with coplanar calibration board near parallel to the imaging plane[J]. Sensors and Actuators A: Physical, 132, 480-486(2006).
[4] Ma C, Pei S S, Sun G L et al. Disparity estimation based on fusion of vision and LiDAR[J]. International Journal of Wavelets, Multiresolution and Information Processing, 20, 2250014(2022).
[5] Xie Q W, Hu X Y, Ren L et al. A binocular vision application in IoT: realtime trustworthy road condition detection system in passable area[J]. IEEE Transactions on Industrial Informatics, 19, 973-983(2023).
[6] Xin Y X, Zhu F T, Shi P F et al. Super-resolution reconstruction algorithm of images based on improved enhanced super-resolution generative adversarial network[J]. Laser & Optoelectronics Progress, 59, 0420002(2022).
[7] Liang M Y, Du J P, Liu H G et al. Video super-resolution reconstruction based on spatio-temporal non-local similarity[J]. Journal of Systems Science and Mathematical Sciences, 36, 1397-1409(2016).
[8] Luo S, Huang H, Zhang K B. Boosting regression-based single-image super-resolution reconstruction[J]. Laser & Optoelectronics Progress, 59, 0810018(2022).
[9] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[10] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).
[13] Dong C, Loy C C, He K M et al. Learning a deep convolutional network for image super-resolution[M]. Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014, 8692, 184-199(2014).
[14] Shi W Z, Caballero J, Huszár F et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C], 1874-1883(2016).
[15] Ledig C, Theis L, Huszár F et al. Photo-realistic single image super-resolution using a generative adversarial network[C], 105-114(2017).
[17] Liang J Y, Sun G L, Zhang K et al. Mutual affine network for spatially variant kernel estimation in blind image super-resolution[C], 4076-4085(2022).
[18] Xie Q W, Liu R R, Sun Z et al. A flexible free-space detection system based on stereo vision[J]. Neurocomputing, 485, 252-262(2022).
[19] Lu W W, Liu A D, Qiu X et al. A moving-horizon-based method for robot pose estimation of vision[J]. Journal of Systems Science and Mathematical Sciences, 41, 1772-1787(2021).
[20] Wei X H, Tang C Y, Wang B et al. Three-dimensional cooperative target structure design and location algorithm for vision landing[J]. Systems Engineering-Theory & Practice, 39, 2975-2983(2019).
[21] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 60, 91-110(2004).
[22] Tian Y R, Fan B, Wu F C. L2-net: deep learning of discriminative patch descriptor in euclidean space[C], 6128-6136(2017).
[23] DeTone D, Malisiewicz T, Rabinovich A. SuperPoint: self-supervised interest point detection and description[C], 337-349(2018).
[24] Zhang Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 1330-1334(2000).
[25] Xie Q W, Long Q, Mita S. Integration of optical flow and Multi-Path-Viterbi algorithm for stereo vision[J]. International Journal of Wavelets, Multiresolution and Information Processing, 15, 1750022(2017).
[26] Liu Z Q, Zhang Y R, Zhao J X et al. High speed camera calibration for velocity measurement in range static explosion experiment[J]. Laser & Optoelectronics Progress, 53, 111501(2016).