[1] BAKER S, KANADE T. Limits on super resolution and how to break them[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 24, 1167-1183(2002).

[2] DONG C, LOY C C, HE K et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 38, 295-307(2016).

[3] DONG C, LOY C C, TANG X et al. Accelerating the super-resolution convolutional neural network[C], 391-407(2016).

[4] KIM J, LEE J K, LEE K M et al. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).

[5] LEDIG C, THEIS L, HUSZAR F et al. Photo-realistic single image super-resolution using a generative adversarial network[C], 105-114(2017).

[6] HUI Z, GAO X, YANG Y et al. Lightweight image super-resolution with information multi-distillation network[C], 2024-2032(2019).

[7] LI Z, LIU Y, CHEN X et al. Blueprint separable residual network for efficient image super-resolution[C], 833-843(2022).

[8] HU Y, HUANG Y, ZHANG K. Multi-scale information distillation network for efficient image super-resolution[J]. Knowledge-Based Systems, 275, 110718(2023).

[9] WANG H, CHEN X, NI B et al. Omni aggregation networks for lightweight image super-resolution[C], 22378-22387(2023).

[10] SUN L, DONG J, TANG J et al. Spatially-adaptive feature modulation for efficient image super-resolution[C], 13190-13199(2023).

[11] HE Z, TANG S, YANG J et al. Cascaded deep networks with multiple receptive fields for infrared image super-resolution[J]. IEEE Transactions on Circuits and Systems for Video Technology, 29, 2310-2322(2018).

[12] ZOU Y, ZHANG L, LIU C et al. Super-resolution reconstruction of infrared images based on a convolutional neural network with skip connections[J]. Optics and Lasers in Engineering, 146, 106717(2021).

[13] YUAN Xilin, ZHANG Baohui, ZHANG Qian et al. Infrared images with super-resolution based on deep convolutional neural network[J]. Infrared Technology, 45, 498-505(2023).

[14] BAI Hao, BAI Tingzhu. Infrared image super-resolution reconstruction algorithm based on deep residual neural network[J]. Infrared Technology, 46, 176-182(2024).

[15] JIANG Mengjie, QIAN Wenhua, XU Dan et al. Gradual model reconstruction of Dongba painting based on residual dense structure[J]. Journal of Image and Graphics, 27, 1084-1096(2022).

[16] SHI Yifeng, CHEN Nan, ZHU Fang et al. Single-frame infrared image super-resolution reconstruction for real scenes[J]. Infrared Technology, 46, 427-436(2024).

[17] LIM B, SON S, KIM H et al. Enhanced deep residual networks for single image super-resolution[C], 136-144(2017).

[18] RIVADENEIRA R E, SAPPA A D, VINTIMILLA B X. Thermal image super-resolution: a novel architecture and dataset[C], 111-119(2020).

[19] SHEIKH H R, SABIR M F, BOVIK A C. A statistical evaluation of recent full reference image quality assessment algorithms[J]. IEEE Transactions on Image Processing, 15, 3440-3451(2006).

[20] WANG Z, BOVIK A C, SHEIKH H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004).