Abstract
Infrared image is widely used in military, medical, monitoring security and other fields. Due to the limitation of hardware devices, infrared image has the problems of low signal-to-noise ratio, blurred edge and low contrast. In view of the above problems, In this paper, a super-resolution reconstruction method of infrared image based on mixed convolution multi-scale residual network is proposed. Through the multi-scale residual network to improve the utilization of features, the mixed convolution is introduced into the multi-scale residual network, which can increase the receptive field without changing the size of the feature map and eliminate the blind spots. The extracted features are fused by recursive fusion to improve the utilization of features. Through experiments and tests on multiple infrared image data sets, Through the test on the infrared image data set show that the proposed method can improve the infrared image edge information, fully extract the texture details from the infrared image, and suppress noise. The objective index of the reconstructed infrared image is mainly better than that of the contrast method, and can still achieve a better reconstruction effect in the real scene.
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The data that support the findings of this study are available on request from the corresponding author upon reasonable request.
References
Alain H, Ziou D (2010) Image quality metrics: PSNR vs. SSIM, 2010 20th international conference on pattern recognition. Istanbul. https://doi.org/10.1109/ICPR.2010.579
Alejandro G, Fang Z, Yainuvis S et al (2016) Pedestrian detection at day/night time with visible and FIR cameras: a comparison. Sensors 16(6):820. https://doi.org/10.3390/s16060820
Andreas NS (1997) Space-based infrared system (SBIRS) system of systems. IEEE Aerospace Conf:429–438
Anwar S, Khan S, Barnes N (2020) A deep journey into super-resolution: a survey. ACM Comput Surv 53(3):1–34. https://doi.org/10.1145/3390462
Baker S, Kanade T (2002) Limits on super-resolution and how to break them. IEEE Trans Pattern Anal Mach Intell 24(9):1167–1183. https://doi.org/10.1109/cvpr.2000.854852
Baker S, Kanade T (2002) Limits on super-resolution and how to break them. IEEE Trans Pattern Anal Mach Intell 24(9):1167–1183. https://doi.org/10.1109/cvpr.2000.854852
Dai T, Cai J, Zhang Y, Xia S, Zhang L (2019) Second-Order Attention Network for Single Image Super-Resolution, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2019.01132
Dong C, Loy C, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. Eur Conf Comp Vision (ECCV) 8692:184–199. https://doi.org/10.1007/978-3-319-10593-2_13
Elad M, Feuer A (1997) Restoration of a single super-resolution image from several blurred, noisy, and under-sampled measured images. IEEE Trans Image Process 6(12):1646–1658. https://doi.org/10.1109/83.650118
Elad M, Feuer A (2002) Restoration of a single super-resolution image from several blurred, Noisy, and under sampled measured images. IEEE Trans Image Process 6(12):1646–1658
Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial networks. Proceed 27th Int Conf Neural Inform Process Syst 2(14):2672–2680
He Z, Tang S, Yang J, Cao Y, Ying Yang M, Cao Y (2019) Cascaded deep networks with multiple receptive fields for infrared image super-resolution. IEEE Trans Circuits Syst Video Technol 29(8):2310–2322. https://doi.org/10.1109/tcsvt.2018.2864777
Justin J, Alexandre A, Li F (2016) Perceptual losses for real-time style transfer and super-resolution. Proceed Eur Conf Comp Vision 694–711. https://doi.org/10.1007/978-3-319-46475-6_43
Kermany D, Goldbaum M, Cai W et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131. https://doi.org/10.1016/j.cell.2018.02.010
Kim K, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. IEEE Trans Pattern Anal Mach Intell 32(6):1127–1133. https://doi.org/10.1109/tpami.2010.25
Kim J, Lee J, Lee K (2016) Deeply-recursive convolutional network for image super-resolution. IEEE Conf Comput Vision Patt Recog (CVPR) 1637-1645. https://doi.org/10.1109/cvpr.2016.181
Kingma D, Ba J (2014) Adam: a method for stochastic optimization, computer science. arXiv preprint arXiv:1412.6980
Kuang X, Sui X, Liu Y, Chen Q, Gu G (2019) Single infrared image enhancement using a deep convolutional neural network. Neurocomputing 332(7):119–128. https://doi.org/10.1016/j.neucom.2018.11.081
Lahiri BB, Bagavathiappan S, Jayakumar T et al (2012) Medical applications of infrared thermography: a review. Infrared Phys Technol 55(4):221–235
Lai W, Huang J, Ahuja N, Yang M (2017) Deep Laplacian pyramid networks for fast and accurate super-resolution. IEEE Conf Comp Vision Patt Recogn (CVPR) 5835–5843. https://doi.org/10.1109/CVPR.2017.618
Ledig C, Theis L, Huszar F, Caballero J (2017) Photo-realistic single image super-resolution using a generative adversarial network. IEEE Conf Comp Vision Patt Recogn (CVPR) 105-114. https://doi.org/10.1109/cvpr.2017.19
Li J, Fang F, Mei K, G. (2018) Zhang, multi-scale residual network for image super-resolution. Eur Conf Comput Vision(ECCV) 11212:527–542. https://doi.org/10.1007/978-3-030-01237-3_32
Liu F, Han P, Wang Y, Li X, Bai L, Shao X (2016) Super resolution reconstruction of infrared images based on classified dictionary learning. Infrared Phys Technol 76:139–147. https://doi.org/10.1016/j.infrared.2018.03.008
Ma J, Ma J, Ma Y, Li C (2019) Infrared and visible image fusion methods and applications: a survey. Inform Fusion 45:153–178. https://doi.org/10.1016/j.inffus.2018.02.004
Ma J, Liang Z, Zhang L (2022) A text attention network for spatial deformation robust scene text image super-resolution. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, pp 5901–5910. https://doi.org/10.1109/CVPR52688.2022.00582
Michal I, Shmuel P (1991) Improving resolution by image registration, CVGIP. Graph Models Image Process 53(3):231–239. https://doi.org/10.1016/1049-9652(91)90045-l
Pei Y, Huang Y, Zou Q et al (2021) Effects of image degradation and degradation removal to CNN-based image classification. IEEE Trans Pattern Anal Mach Intell 43(4):1239–1253
Qiu Y, Wang R, Tao D, Cheng J (2019) Embedded block residual network: a recursive restoration model for single-image super-resolution. IEEE/CVF Int Conf Comput Vision (ICCV) 4179-4188. https://doi.org/10.1109/iccv.2019.00428
Qu Z, Jiang P, Zhang W (2020) Development and application of infrared thermography non-destructive testing techniques. Sensors 20(14):3851
Sheikh H, Bovik A (2005) An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans Image Process 14(12):2117–2128. https://doi.org/10.1109/tip.2005.859389
Stark H (1989) High-resolution image recovery from image-plane arrays, using convex projections. J Opt Soc Am A 6(11):1715–1726. https://doi.org/10.1364/josaa.6.001715
Stark H, Oskoui P (1989) High-resolution image recovery from image-plane arrays, using convex projection. J Opt Soc Am A 6(11):1715–1726. https://doi.org/10.1364/josaa.6.001715
Timofte R, De S, Van G (2015) A+: adjusted anchored neighborhood regression for fast super-resolution. Asian Conf Comput Vision 9006:111–126. https://doi.org/10.1007/978-3-319-16817-3_8
Wang Z, Bovik A, Sheikh H et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. https://doi.org/10.1109/tip.2003.819861
Yang T, Tang Y, Lv X, Wang Q, Ke H, Deng Y et al (2017) Size-dependent Ag2S Nan dots for second near-infrared fluorescence/photo acoustics imaging and simultaneous photo thermal therapy. ACS Nano 11(2):1848–1857. https://doi.org/10.1021/acsnano.6b07866.s001
Yang X, Wu W, Liu K, Chen W, Zhou Z (2018) Multiple dictionary pairs learning and sparse representation-based infrared image super-resolution with improved fuzzy clustering. Soft Comput 22:1385–1398. https://doi.org/10.1007/s00500-017-2812-3
Yang F, Yang H, Fu J et al (2020) Learning texture transformer network for image super-resolution, 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.00583
Yu F, Koltun V (2016) Multi-scale context aggregation by dilated convolutions, ICLR, arXiv preprint arXiv:1511.07122v3
Zhang L, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226–2238. https://doi.org/10.1109/tip.2006.877407
Zhang K, Gao X, Tao D, Li X (2012) Single image super-resolution with non-local means and steering kernel regression. IEEE Trans Image Process 21(11):4544–4556. https://doi.org/10.1109/tip.2012.2208977
Zhang Z, Wang X, Jung C (2019) DCSR: dilated convolutions for single image super-resolution. IEEE Trans Image Process 28(4):1625–1635. https://doi.org/10.1109/tip.2018.2877483
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The authors are grateful for collaborative funding support from the Humanity and Social Science Foundation of Ministry of Education, China (21YJAZH077).
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Du, YB., Sun, HM., Zhang, B. et al. A multi-scale mixed convolutional network for infrared image super-resolution reconstruction. Multimed Tools Appl 82, 41895–41911 (2023). https://doi.org/10.1007/s11042-023-15359-0
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DOI: https://doi.org/10.1007/s11042-023-15359-0