非制冷红外无挡片非均匀性校正方法

黄源飞; 黄华

doi:10.11999/JEIT231400

非制冷红外无挡片非均匀性校正方法

doi: 10.11999/JEIT231400

黄源飞,
黄华^,

北京师范大学人工智能学院北京 100875

基金项目: 国家自然科学基金(62202056)

详细信息

作者简介:
黄源飞：男，博士，讲师，研究方向为计算机视觉等

黄华：男，博士，教授，研究方向为计算成像、计算机视觉等

通讯作者:
黄华　huahuang@bnu.edu.cn

中图分类号: TN21;TN911.73
计量
- 文章访问数: 672
- HTML全文浏览量: 376
- PDF下载量: 110
- 被引次数: 0
出版历程
- 收稿日期: 2023-12-19
- 修回日期: 2024-05-11
- 网络出版日期: 2024-05-13
- 刊出日期: 2024-05-30

Shutter-less Non-uniformity Correction Methods in Uncooled Infrared Imagery

HUANG Yuanfei,
HUANG Hua^,

School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China

Funds: The National Natural Science Foundation of China (62202056)

摘要

摘要: 受成像原理及加工工艺的限制，非制冷红外探测器存在严重的非均匀性，为了提升红外成像质量，必须对图像进行非均匀性校正。依据成因和分布特点，该文将红外非均匀性分为低频非均匀性、散粒非均匀性和条纹非均匀性3类，并从探测器的光学系统、热敏材料、放大电路等方面探究了非制冷红外成像非均匀性的形成机理。之后，该文系统性地总结目前无挡片非均匀性校正方法，根据方法的工作原理，将其归纳为基于统计的、基于滤波的、基于优化的和基于学习的非均匀性校正方法4类，并根据每类方法在处理不同非均匀性时的特异性进行梳理和总结。最后，本文对现阶段非制冷红外无挡片非均匀性校正方法存在的问题进行了回顾和总结，并对面向实际应用的非均匀性校正方法发展趋势进行了展望。
- 非制冷红外成像 /
- 非均匀性校正 /
- 成像机理 /
- 图像去噪
Abstract: Due to the limitations of imaging principles and processing technology, uncooled infrared imagery detectors suffer from serious non-uniformities, damaging the imaging results. To improve the quality of infrared images, non-uniformity correction techniques are of great significance for practical applications. According to the physical formation and spatial characteristics of the non-uniformity in uncooled infrared imagery detectors, this paper divides these common non-uniformities into three categories: low-frequency non-uniformity, shot non-uniformity, and stripe non-uniformity. The physical mechanisms of these non-uniformities are further explored from the procedure in the optical system, thermal materials, and amplifier circuit of the uncooled infrared imagery detector. Then, the existing shutter-less non-uniformity correction methods are systematically summarized. Based on the principles of the methods, statistical-based, filter-based, optimization-based, and learning-based non-uniformity correction methods are categorized. Besides, the specifics of each method in dealing with different non-uniformities are clarified and distinguished. Finally, the existing problems in the current methods are reviewed and summarized, and the development trend of non-uniformity correction methods in future practical applications is prospected.
- Uncooled infrared imagery /
- Non-uniformity correction /
- Imaging mechanism /
- Image denoising

HTML全文

图 1 非均匀性对非制冷红外成像效果的影响

下载: 全尺寸图片幻灯片

图 2 非制冷红外探测器的成像非均匀性形成机理

下载: 全尺寸图片幻灯片

图 3 低频非均匀性成像机理的仿真验证结果^[19]

下载: 全尺寸图片幻灯片

图 4 条纹非均匀性的低秩特性^[22]

下载: 全尺寸图片幻灯片

图 5 无挡片非均匀性校正方法分类

下载: 全尺寸图片幻灯片

图 6 多项式曲面模拟的红外低频非均匀性

下载: 全尺寸图片幻灯片

图 7 基于统计的红外非均匀性校正方法处理效果

下载: 全尺寸图片幻灯片

图 8 动态场景下场景信息与非均匀性像素的时序统计

下载: 全尺寸图片幻灯片

图 9 基于滤波的红外非均匀性校正方法处理效果

下载: 全尺寸图片幻灯片

图 10 先验项对优化方向的重要性

下载: 全尺寸图片幻灯片

图 11 基于优化的红外非均匀性校正方法处理效果

下载: 全尺寸图片幻灯片

图 12 非均匀性校正网络模型训练的流程图

下载: 全尺寸图片幻灯片

图 13 基于学习的红外非均匀性校正方法处理效果

下载: 全尺寸图片幻灯片

图 14 非制冷红外无挡片非均匀性校正方法发展趋势

下载: 全尺寸图片幻灯片

表 1 现阶段非均匀性校正方法的特点及适用场景

非均匀性校正方法	适用场景	优势	局限性
统计类	单一、简单的成像非均匀性	快速、简单	存在非均匀性残留；场景突变时易产生鬼影
滤波类	周期性的成像非均匀性	快速、稳定	易出现模糊效应或伪影；场景突变时易产生鬼影
优化类	非均匀性先验已知	灵活、校正效果好	严重依赖先验信息；收敛速度慢、实时性差
学习类	具备大规模成对训练样本	校正效果好	训练时间长、泛化性差

下载: 导出CSV

表 2 部分国产非制冷红外探测器技术参数统计

探测器厂商	探测器型号	传感器尺寸(mm³)	成像分辨率	采集帧频(Hz)
艾睿光电	RTDS121C	$39.9 \times 33.5 \times 8.53$	$1280 \times 1024$	30/50/60
艾睿光电	RTDF081M	$41 \times 31.5 \times 8.31$	$1920 \times 1080$	$ \leqslant $30
大立科技	DLE1280	$38 \times 29 \times 8.6$	$1280 \times 1024$	60
大立科技	DLE1920	$45 \times 42.5 \times 8.6$	$1920 \times 1080$	60
高德红外	COIN612R	$25.4 \times 25.4 \times 14.1$	$640 \times 512$	30
高德红外	GST1212M	$45 \times 28.5 \times 8$	$1280 \times 1024$	50

下载: 导出CSV

参考文献(102)

[1]	刘靳, 姬红兵. 基于非平稳背景下的红外小目标检测[J]. 电子与信息学报, 2010, 32(6): 1295–1300. doi: 10.3724/SP.J.1146.2009.01083. LIU Jin and JI Hongbing. IR small targets detection based on non-homogeneous background[J]. Journal of Electronics & Information Technology, 2010, 32(6): 1295–1300. doi: 10.3724/SP.J.1146.2009.01083.
[2]	余黎静, 唐利斌, 杨文运, 等. 非制冷红外探测器研究进展(特邀)[J]. 红外与激光工程, 2021, 50(1): 20211013. doi: 10.3788/IRLA20211013. YU Lijing, TANG Libin, YANG Wenyun, et al. Research progress of uncooled infrared detectors (Invited)[J]. Infrared and Laser Engineering, 2021, 50(1): 20211013. doi: 10.3788/IRLA20211013.
[3]	HE Zewei, CAO Yanpeng, DONG Yafei, et al. Single-image-based nonuniformity correction of uncooled long-wave infrared detectors: A deep-learning approach[J]. Applied Optics, 2018, 57(18): D155–D164. doi: 10.1364/AO.57.00D155.
[4]	姚婷, 梁成文, 李凯扬. 探测器温度对非制冷红外热像仪人体测温的影响与修正[J]. 红外技术, 2016, 38(11): 984–989. YAO Ting, LIANG Chengwen, and LI Kaiyang. Effect of detector temperature on the human body temperature measurement of uncooled infrared thermal imager and its correction[J]. Infrared Technology, 2016, 38(11): 984–989.
[5]	JIN Yan, JIANG Jie, and ZHANG Guangjun. Three-step nonuniformity correction for a highly dynamic intensified charge-coupled device star sensor[J]. Optics Communications, 2012, 285(7): 1753–1758. doi: 10.1016/j.optcom.2011.12.043.
[6]	CHANG Songtao and LI Zhou. Single-reference-based solution for two-point nonuniformity correction of infrared focal plane arrays[J]. Infrared Physics & Technology, 2019, 101: 96–104. doi: 10.1016/j.infrared.2019.06.007.
[7]	王成龙, 王春阳, 谷健, 等. 一种基于定标的非均匀性校正改进算法[J]. 中国光学, 2022, 15(3): 498–507. doi: 10.37188/CO.2021-0231. WANG Chenglong, WANG Chunyang, GU Jian, et al. An improved non-uniformity correction algorithm based on calibration[J]. Chinese Optics, 2022, 15(3): 498–507. doi: 10.37188/CO.2021-0231.
[8]	ZUO Chao, CHEN Qian, GU Guohua, et al. Scene-based nonuniformity correction algorithm based on interframe registration[J]. Journal of the Optical Society of America A, 2011, 28(6): 1164–1176. doi: 10.1364/JOSAA.28.001164.
[9]	RONG Shenghui, ZHOU Huixin, ZHAO Dong, et al. Infrared fix pattern noise reduction method based on shearlet transform[J]. Infrared Physics & Technology, 2018, 91: 243–249. doi: 10.1016/j.infrared.2018.05.002.
[10]	CAO Yanpeng, HE Zewei, YANG Jiangxin, et al. Spatially adaptive column fixed-pattern noise correction in infrared imaging system using 1d horizontal differential statistics[J]. IEEE Photonics Journal, 2017, 9(5): 1–13. doi: 10.1109/JPHOT.2017.2752000.
[11]	SONG Lingfei and HUANG Hua. Spatial and temporal adaptive nonuniformity correction for infrared focal plane arrays[J]. Optics Express, 2022, 30(25): 44681–44700. doi: 10.1364/OE.471825.
[12]	LIU Tong, SUI Xiubao, WANG Yihong, et al. Strong non-uniformity correction algorithm based on spectral shaping statistics and LMS[J]. Optics Express, 2023, 31(19): 30693–30709. doi: 10.1364/OE.496398.
[13]	GOYAL P. Review of infrared signal processing algorithms[J]. International Journal of Computer Science and Technology, 2011, 2(2): 176–180.
[14]	WANMALI S and SHEKOKAR R. Survey on some scene based nonuniformity correction algorithms for infrared images[J]. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Co/*10.17148/IJIREEICE. 2017.5421.
[15]	董立泉, 金伟其, 隋婧. 基于场景的红外焦平面阵列非均匀性校正算法综述[J]. 光学技术, 2008, 34(S1): 112–118. doi: 10.13741/j.cnki.11-1879/o4.2008.s1.068. DONG Liquan, JIN Weiqi, and SUI Jing. Study on the scene-based non-unifonnitV correction algorithms for IRFPA[J]. Optical Technique, 2008, 34(S1): 112–118. doi: 10.13741/j.cnki.11-1879/o4.2008.s1.068.
[16]	YU Yuan, LEE B G, PIKE M, et al. Deep learning-based RGB-thermal image denoising: Review and applications[J]. Multimedia Tools and Applications, 2024, 83(4): 11643–11641. doi: 10.1007/s11042-023-15916-7.
[17]	刘子骥. 非制冷红外焦平面探测器测试及验证成像技术研究[D]. [博士论文], 电子科技大学, 2013. LIU Ziji. Study on uncooled infrared focal plane detector testing and imaging technology[D]. [Ph. D. dissertation], University of Electronic Science and Technology of China, 2013.
[18]	LI Yiyang, JIN Weiqi, and LIU Zhihao. Interior radiation noise reduction method based on multiframe processing in infrared focal plane arrays imaging system[J]. IEEE Photonics Journal, 2018, 10(5): 1–12. doi: 10.1109/JPHOT.2018.2865224.
[19]	王帅, 赵耀宏, 向伟. 单帧红外图像低频非均匀性噪声校正算法[J]. 计算机辅助设计与图形学学报, 2020, 32(5): 811–819. doi: 10.3724/SP.J.1089.2020.17890. WANG Shuai, ZHAO Yaohong, and XIANG Wei. Single image based nonuniformity correction method in infrared camera[J]. Journal of Computer-Aided Design & Computer Graphics, 2020, 32(5): 811–819. doi: 10.3724/SP.J.1089.2020.17890.
[20]	MAGGIONI M, SÁNCHEZ-MONGE E, and FOI A. Joint removal of random and fixed-pattern noise through spatiotemporal video filtering[J]. IEEE Transactions on Image Processing, 2014, 23(10): 4282–4296. doi: 10.1109/TIP.2014.2345261.
[21]	SONG Lingfei and HUANG Hua. Fixed pattern noise removal based on a semi-calibration method[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(10): 11842–11855. doi: 10.1109/TPAMI.2023.3274826.
[22]	CHANG Yi, YAN Luxin, WU Tao, et al. Remote sensing image stripe noise removal: From image decomposition perspective[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12): 7018–7031. doi: 10.1109/TGRS.2016.2594080.
[23]	SONG Lingfei and HUANG Hua. Simultaneous destriping and image denoising using a nonparametric model with the EM algorithm[J]. IEEE Transactions on Image Processing, 2023, 32: 1065–1077. doi: 10.1109/TIP.2023.3239193.
[24]	史浩然, 沈同圣, 李召龙, 等. 红外系统中渐晕效应的模拟方法研究[J]. 红外技术, 2015, 37(4): 296–299. SHI Haoran, SHEN Tongsheng, LI Zhaolong, et al. Simulation of the vignetting effect in infrared imaging system[J]. Infrared Technology, 2015, 37(4): 296–299.
[25]	WU Zimu and WANG Xia. Non-uniformity correction for medium wave infrared focal plane array-based compressive imaging[J]. Optics Express, 2020, 28(6): 8541–8559. doi: 10.1364/OE.381523.
[26]	KUANG Xiaodong, SUI Xiubao, LIU Yuan, et al. Single infrared image optical noise removal using a deep convolutional neural network[J]. IEEE Photonics Journal, 2018, 10(2): 7800615. doi: 10.1109/JPHOT.2017.2779149.
[27]	隋修宝. 非制冷凝视热像仪成像理论以及关键技术研究[D]. [博士论文], 南京理工大学, 2009. SUI Xiubao. Research on the imaging theory and the key techniques of uncooled staring thermal imager[D]. [Ph. D. dissertation]. Nanjing University of Science and Technology, 2009.
[28]	ABDEL-RAHMAN M, ILAHI S, ZIA M F, et al. Temperature coefficient of resistance and thermal conductivity of Vanadium oxide ‘Big Mac’ sandwich structure[J]. Infrared Physics & Technology, 2015, 71: 127–130. doi: 10.1016/j.infrared.2015.03.006.
[29]	GENG Lixiang, CHEN Qian, and QIAN Weixian. An adjacent differential statistics method for IRFPA nonuniformity correction[J]. IEEE Photonics Journal, 2013, 5(6): 6801615. doi: 10.1109/JPHOT.2013.2293614.
[30]	CAO Yanpeng and LI Yiqun. Strip non-uniformity correction in uncooled long-wave infrared focal plane array based on noise source characterization[J]. Optics Communications, 2015, 339: 236–242. doi: 10.1016/j.optcom.2014.10.041.
[31]	CAO Yanpeng and TISSE C L. Single-image-based solution for optics temperature-dependent nonuniformity correction in an uncooled long-wave infrared camera[J]. Optics Letters, 2014, 39(3): 646–648. doi: 10.1364/OL.39.000646.
[32]	TINCHER M, MEYER C R, GUPTA R, et al. Polynomial modeling and reduction of RF body coil spatial inhomogeneity in MRI[J]. IEEE Transactions on Medical Imaging, 1993, 12(2): 361–365. doi: 10.1109/42.232267.
[33]	LIEW A W C and YAN Hong. An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation[J]. IEEE Transactions on Medical Imaging, 2003, 22(9): 1063–1075. doi: 10.1109/TMI.2003.816956.
[34]	SRIPRAGASH L and SUNDARESAN M. Non-uniformity correction and sound zone detection in pulse thermographic nondestructive evaluation[J]. NDT & E International, 2017, 87: 60–67. doi: 10.1016/j.ndteint.2017.01.006.
[35]	TASDIZEN T, JURRUS E, and WHITAKER R T. Non-uniform illumination correction in transmission electron microscopy[C]. MICCAI Workshop on Microscopic Image Analysis with Applications in Biology, New York, USA, 2008: 5–6.
[36]	GARCÍA-SEBASTIÁN M, FERNÁNDEZ E, GRAÑA M, et al. A parametric gradient descent MRI intensity inhomogeneity correction algorithm[J]. Pattern Recognition Letters, 2007, 28(13): 1657–1666. doi: 10.1016/j.patrec.2007.04.016.
[37]	SHI Yu, CHEN Jisong, HONG Hanyu, et al. Multi-scale thermal radiation effects correction via a fast surface fitting with Chebyshev polynomials[J]. Applied Optics, 2022, 61(25): 7498–7507. doi: 10.1364/AO.465157.
[38]	HONG Hanyu, LIU Jiakang, SHI Yu, et al. Progressive nonuniformity correction for aero-optical thermal radiation images via bilateral filtering and bézier surface fitting[J]. IEEE Photonics Journal, 2023, 15(2): 7800611. doi: 10.1109/JPHOT.2023.3250949.
[39]	HARRIS J G and CHIANG Y M. Nonuniformity correction of infrared image sequences using the constant-statistics constraint[J]. IEEE Transactions on Image Processing, 1999, 8(8): 1148–1151. doi: 10.1109/83.777098.
[40]	ZHANG Chao and ZHAO Wenyi. Scene-based nonuniformity correction using local constant statistics[J]. Journal of the Optical Society of America A, 2008, 25(6): 1444–1453. doi: 10.1364/JOSAA.25.001444.
[41]	REDLICH R, FIGUEROA M, TORRES S N, et al. Embedded nonuniformity correction in infrared focal plane arrays using the constant range algorithm[J]. Infrared Physics & Technology, 2015, 69: 164–173. doi: 10.1016/j.infrared.2015.01.026.
[42]	QIAN Weixian, CHEN Qian, and GU Guohua. The high-frequency constant-statistics constraint nonuniformity correction algorithm[J]. Journal of Infrared, Millimeter, and Terahertz Waves, 2011, 32(6): 778–792. doi: 10.1007/s10762-011-9793-6.
[43]	LIU Chengwei, SUI Xiubao, LIU Yuan, et al. FPN estimation based nonuniformity correction for infrared imaging system[J]. Infrared Physics & Technology, 2019, 96: 22–29. doi: 10.1016/j.infrared.2018.09.025.
[44]	PIPA D R, DA SILVA E A B, PAGLIARI C L, et al. Recursive algorithms for bias and gain nonuniformity correction in infrared videos[J]. IEEE Transactions on Image Processing, 2012, 21(12): 4758–4769. doi: 10.1109/TIP.2012.2218820.
[45]	DELON J. Midway image equalization[J]. Journal of Mathematical Imaging and Vision, 2004, 21(2): 119–134. doi: 10.1023/B:JMIV.0000035178.72139.2d.
[46]	康长青, 张其林, 郑毅, 等. 基于中间均衡直方图的红外图像非均匀性校正[J]. 激光与红外, 2013, 43(11): 1240–1242. doi: 10.3969/j.issn.1001-5078.2013.11.08. KANG Changqing, ZHANG Qilin, ZHENG Yi, et al. Non uniformity correction algorithm for IR images based on midway equalization histogram[J]. Laser & Infrared, 2013, 43(11): 1240–1242. doi: 10.3969/j.issn.1001-5078.2013.11.08.
[47]	简献忠, 陆睿智, 郭强. 改进的单幅红外图像局部自适应非均匀校正[J]. 激光与红外, 2014, 44(12): 1344–1348. doi: 10.3969/j.issn.1001-5078.2014.12.011. JIAN Xianzhong, LU Ruizhi, and GUO Qiang. Modified locally adaptive non-uniformity correction algorithm for single infrared image[J]. Laser & Infrared, 2014, 44(12): 1344–1348. doi: 10.3969/j.issn.1001-5078.2014.12.011.
[48]	GADALLAH F L, CSILLAG F, and SMITH E J M. Destriping multisensor imagery with moment matching[J]. International Journal of Remote Sensing, 2000, 21(12): 2505–2511. doi: 10.1080/01431160050030592.
[49]	CHEN Jinsong, SHAO Yun, GUO Huadong, et al. Destriping CMODIS data by power filtering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9): 2119–2124. doi: 10.1109/TGRS.2003.817206.
[50]	KANG Yifei, PAN Li, SUN Mingwei, et al. Destriping high-resolution satellite imagery by improved moment matching[J]. International Journal of Remote Sensing, 2017, 38(22): 6346–6365. doi: 10.1080/01431161.2017.1353162.
[51]	韩玲, 董连凤, 张敏, 等. 基于改进的矩匹配方法高光谱影像条带噪声滤波技术[J]. 光学学报, 2009, 29(12): 3333–3338. doi: 10.3788/AOS20092912.3333. HAN Ling, DONG Lianfeng, ZHANG Min, et al. Destriping hyperspectral image based on an improved moment matching method[J]. Acta Optica Sinica, 2009, 29(12): 3333–3338. doi: 10.3788/AOS20092912.3333.
[52]	FARBMAN Z, FATTAL R, LISCHINSKI D, et al. Edge-preserving decompositions for multi-scale tone and detail manipulation[J]. ACM Transactions on Graphics, 2008, 27(3): 1–10. doi: 10.1145/1360612.1360666.
[53]	蔡秀梅, 马今璐, 吴成茂, 等. 基于模糊同态滤波的彩色图像增强算法[J]. 计算机仿真, 2020, 37(6): 342–346. doi: 10.3969/j.issn.1006-9348.2020.06.070. CAI Xiumei, MA Jinlu, WU Chengmao, et al. Color image enhancement algorithm based on fuzzy homomorphic filtering[J]. Computer Simulation, 2020, 37(6): 342–346. doi: 10.3969/j.issn.1006-9348.2020.06.070.
[54]	梁琳, 何卫平, 雷蕾, 等. 光照不均图像增强方法综述[J]. 计算机应用研究, 2010, 27(5): 1625–1628. doi: 10.3969/j.issn.1001-3695.2010.05.006. LIANG Lin, HE Weiping, LEI Lei, et al. Survey on enhancement methods for non-uniform illumination image[J]. Application Research of Computers, 2010, 27(5): 1625–1628. doi: 10.3969/j.issn.1001-3695.2010.05.006.
[55]	SOLBØ S and ELTOFT T. Homomorphic wavelet-based statistical despeckling of SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4): 711–721. doi: 10.1109/TGRS.2003.821885.
[56]	TORRES S N, HAYAT M M, ARMSTRONG E E, et al. Kalman-filtering approach for nonuniformity correction in focal plane array sensors[C]. The SPIE, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XI, Orlando, USA, 2000: 196–205. doi: 10.1117/12.391780.
[57]	ROSSI A, DIANI M, and CORSINI G. Bilateral filter-based adaptive nonuniformity correction for infrared focal-plane array systems[J]. Optical Engineering, 2010, 49(5): 057003. doi: 10.1117/1.3425660.
[58]	RONG Shenghui, ZHOU Huixin, QIN Hanlin, et al. Guided filter and adaptive learning rate based non-uniformity correction algorithm for infrared focal plane array[J]. Infrared Physics & Technology, 2016, 76: 691–697. doi: 10.1016/j.infrared.2016.04.037.
[59]	LAI Rui, YUE Gaoyu, and ZHANG Gangxuan. Total variation based neural network regression for nonuniformity correction of infrared images[J]. Symmetry, 2018, 10(5): 157. doi: 10.3390/sym10050157.
[60]	杨硕, 赵保军, 毛二可, 等. 基于PM扩散的红外焦平面阵列神经网络非均匀校正算法[J]. 电子与信息学报, 2013, 35(11): 2744–2750. doi: 10.3724/SP.J.1146.2012.01051. YANG Shuo, ZHAO Baojun, MAO Erke, et al. Neural network non-uniformity correction for infrared focal plane array based on perona malik diffusion[J]. Journal of Electronics & Information Technology, 2013, 35(11): 2744–2750. doi: 10.3724/SP.J.1146.2012.01051.
[61]	HARDIE R C, BAXLEY F, BRYS B, et al. Scene-based nonuniformity correction with reduced ghosting using a Gated LMS algorithm[J]. Optics Express, 2009, 17(17): 14918–14933. doi: 10.1364/OE.17.014918.
[62]	VERA E and TORRES S. Fast adaptive nonuniformity correction for infrared focal-plane array detectors[J]. EURASIP Journal on Advances in Signal Processing, 2005, 2005: 560759. doi: 10.1155/ASP.2005.1994.
[63]	GODOY S E, PEZOA J E, and TORRES S N. Noise cancellation based nonuniformity correction algorithm for infrared focal-plane arrays[J]. Applied Optics, 2008, 47(29): 5394–5399. doi: 10.1364/AO.47.005394.
[64]	ZUO Chao, CHEN Qian, GU Guohua, et al. New temporal high-pass filter nonuniformity correction based on bilateral filter[J]. Optical Review, 2011, 18(2): 197–202. doi: 10.1007/s10043-011-0042-y.
[65]	HUANG Jun, MA Yong, FAN Fan, et al. A scene-based nonuniformity correction algorithm based on fuzzy logic[J]. Optical Review, 2015, 22(4): 614–622. doi: 10.1007/s10043-015-0107-4.
[66]	MORRIS N J W, AVIDAN S, MATUSIK W, et al. Statistics of infrared images[C]. 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, USA, 2007: 1–7. doi: 10.1109/CVPR.2007.383003.
[67]	CAO Yanpeng, YANG M Y, and TISSE C L. Effective strip noise removal for low-textured infrared images based on 1-D guided filtering[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 26(12): 2176–2188. doi: 10.1109/TCSVT.2015.2493443.
[68]	LIU Na, LI Wei, TAO Ran, et al. Wavelet-domain low-rank/group-sparse destriping for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12): 10310–10321. doi: 10.1109/TGRS.2019.2933555.
[69]	ZHENG Yuanjie, YU Jingyi, KANG S B, et al. Single-image vignetting correction using radial gradient symmetry[C]. 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, USA, 2008: 1–8. doi: 10.1109/CVPR.2008.4587413.
[70]	SHI Yu, HONG Hanyu, HUA Xia, et al. Aero-optic thermal radiation effects correction with a low-frequency prior and a sparse constraint in the gradient domain[J]. Journal of the Optical Society of America A, 2019, 36(9): 1566–1572. doi: 10.1364/JOSAA.36.001566.
[71]	LIU Li and ZHANG Tianxu. Intensity non-uniformity correction of aerothermal images via ℓ_p-regularized minimization[J]. Journal of the Optical Society of America A, 2016, 33(11): 2206–2212. doi: 10.1364/JOSAA.33.002206.
[72]	LIU Li and ZHANG Tianxu. Optics temperature-dependent nonuniformity correction via ℓ₀-regularized prior for airborne infrared imaging systems[J]. IEEE Photonics Journal, 2016, 8(5): 3900810. doi: 10.1109/JPHOT.2016.2602059.
[73]	LI Zhenhua, XU Guili, CHENG Yuehua, et al. A structure prior weighted hybrid ℓ₂-ℓ_p variational model for single infrared image intensity nonuniformity correction[J]. Optik, 2021, 229: 165867. doi: 10.1016/j.ijleo.2020.165867.
[74]	WANG Yu, WANG Yihong, LIU Tong, et al. Enhancing infrared imaging systems with temperature-dependent nonuniformity correction via single-frame and inter-frame structural similarity[J]. Applied Optics, 2023, 62(26): 7075–7082. doi: 10.1364/AO.497228.
[75]	MIAO Xinyuan, ZHANG Ye, and ZHANG Junping. Thermal hyperspectral image denoising using total variation based on bidirectional estimation and brightness temperature smoothing[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 7001205. doi: 10.1109/LGRS.2021.3066627.
[76]	WAN Minjie, GU Guohua, XU Yunkai, et al. Total variation-based interframe infrared patch-image model for small target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 7003305. doi: 10.1109/LGRS.2021.3126772.
[77]	GU Shuhang, ZHANG Lei, ZUO Wangmeng, et al. Weighted nuclear norm minimization with application to image denoising[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 2862–2869. doi: 10.1109/CVPR.2014.366.
[78]	CHEN Yang, CAO Wenfei, PANG Li, et al. Hyperspectral image denoising via texture-preserved total variation regularizer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5516114. doi: 10.1109/TGRS.2023.3292518.
[79]	BOUALI M and LADJAL S. Toward optimal destriping of MODIS data using a unidirectional variational model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(8): 2924–2935. doi: 10.1109/TGRS.2011.2119399.
[80]	CHANG Yi, YAN Luxin, FANG Houzhang, et al. Simultaneous destriping and denoising for remote sensing images with unidirectional total variation and sparse representation[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(6): 1051–1055. doi: 10.1109/LGRS.2013.2285124.
[81]	HE Wei, ZHANG Hongyan, ZHANG Liangpei, et al. Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 178–188. doi: 10.1109/TGRS.2015.2452812.
[82]	HU Ting, LI Wei, LIU Na, et al. Hyperspectral image restoration using adaptive anisotropy total variation and nuclear norms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(2): 1516–1533. doi: 10.1109/TGRS.2020.2999634.
[83]	LIU Li, XU Luping, and FANG Houzhang. Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(3): 1777–1789. doi: 10.1109/TGRS.2019.2948601.
[84]	ZHANG Kai, ZUO Wangmeng, CHEN Yunjin, et al. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142–3155. doi: 10.1109/TIP.2017.2662206.
[85]	TIAN Chunwei, FEI Lunke, ZHENG Wenxian, et al. Deep learning on image denoising: An overview[J]. Neural Networks, 2020, 131: 251–275. doi: 10.1016/j.neunet.2020.07.025.
[86]	CHANG Yi, YAN Luxin, LIU Li, et al. Infrared aerothermal nonuniform correction via deep multiscale residual network[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(7): 1120–1124. doi: 10.1109/LGRS.2019.2893519.
[87]	HU Xinrui, LUO Shaojuan, HE Chunhua, et al. Infrared thermal image denoising with symmetric multi-scale sampling network[J]. Infrared Physics & Technology, 2023, 134: 104909. doi: 10.1016/j.infrared.2023.104909.
[88]	YANG Pengfei, WU Heng, CHENG Lianglun, et al. Infrared image denoising via adversarial learning with multi-level feature attention network[J]. Infrared Physics & Technology, 2023, 128: 104527. doi: 10.1016/j.infrared.2022.104527.
[89]	GUAN Juntao, LAI Rui, XIONG Ai, et al. Fixed pattern noise reduction for infrared images based on cascade residual attention CNN[J]. Neurocomputing, 2020, 377: 301–313. doi: 10.1016/j.neucom.2019.10.054.
[90]	LIU Kang, CHEN Honglei, BAO Wenzhong, et al. Thermal imaging spatial noise removal via deep image prior and step-variable total variation regularization[J]. Infrared Physics & Technology, 2023, 134: 104888. doi: 10.1016/j.infrared.2023.104888.
[91]	ULYANOV D, VEDALDI A, and LEMPITSKY V. Deep image prior[J]. International Journal of Computer Vision, 2020, 128(7): 1867–1888. doi: 10.1007/s11263-020-01303-4.
[92]	SIMKÓ A, LÖFSTEDT T, GARPEBRING A, et al. MRI bias field correction with an implicitly trained CNN[C/OL]. International Conference on Medical Imaging with Deep Learning, Zurich, Switzerland, 2022: 1125–1138.
[93]	LI Zhuo, LUO Shaojuan, CHEN Meiyun, et al. Infrared thermal imaging denoising method based on second-order channel attention mechanism[J]. Infrared Physics & Technology, 2021, 116: 103789. doi: 10.1016/j.infrared.2021.103789.
[94]	KUANG Xiaodong, SUI Xiubao, CHEN Qian, et al. Single infrared image stripe noise removal using deep convolutional networks[J]. IEEE Photonics Journal, 2017, 9(4): 3900913. doi: 10.1109/JPHOT.2017.2717948.
[95]	LI Jia, ZENG Dan, ZHANG Junjie, et al. Column-spatial correction network for remote sensing image destriping[J]. Remote Sensing, 2022, 14(14): 3376. doi: 10.3390/rs14143376.
[96]	ZHANG Hongyan, CHEN Hongyu, YANG Guangyi, et al. LR-Net: Low-rank spatial-spectral network for hyperspectral image denoising[J]. IEEE Transactions on Image Processing, 2021, 30: 8743–8758. doi: 10.1109/TIP.2021.3120037.
[97]	CHANG Yi, CHEN Meiya, YAN Luxin, et al. Toward universal stripe removal via wavelet-based deep convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4): 2880–2897. doi: 10.1109/TGRS.2019.2957153.
[98]	HE Wei, YAO Quanming, LI Chao, et al. Non-local meets global: An iterative paradigm for hyperspectral image restoration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(4): 2089–2107. doi: 10.1109/TPAMI.2020.3027563.
[99]	PAN Erting, MA Yong, MEI Xiaoguang, et al. Progressive hyperspectral image destriping with an adaptive frequencial focus[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5517312. doi: 10.1109/TGRS.2023.3297622.
[100]	LI Jia, ZHANG Junjie, HAN Jungong, et al. Progressive recurrent neural network for multispectral remote sensing image destriping[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5407318. doi: 10.1109/TGRS.2023.3324606.
[101]	ZHENG Dihan, ZHANG Xiaowen, MA Kaisheng, et al. Learn from unpaired data for image restoration: A variational bayes approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(5): 5889–5903. doi: 10.1109/TPAMI.2022.3215571.
[102]	CROITORU F A, HONDRU V, IONESCU R T, et al. Diffusion models in vision: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(9): 10850–10869. doi: 10.1109/TPAMI.2023.3261988.