Abstract

In this Letter, we present a novel, to the best of our knowledge, line-wise scanning-based super-resolution (LSSR) imaging method. To reduce point spread functions overlapping among pixels, we specifically present a super-resolution (SR) imaging architecture to capture a series of low-resolution images using a line-based optical multiplexing technique, which is able to achieve a good balance between imaging quality and speed. In addition, we propose an efficient joint reconstruction algorithm based on total variation and low-rank constraints to generate a high-resolution image from these low-resolution images that contain different spatial details. Meanwhile, existing stripe noises are efficiently suppressed. Experiments on real data show that LSSR imaging has significant advantages over other state-of-the-art methods in terms of visual quality and quantitative measurement.

© 2022 Optica Publishing Group

Hyperspectral image super-resolution based on the transfer of both spectra and multi-level features

Xuheng Cao, Yusheng Lian, Zilong Liu, Han Zhou, Xiangmei Hu, Beiqing Huang, and Wan Zhang
Opt. Lett. 47(14) 3431-3434 (2022)

Efficient sub-pixel convolutional neural network for terahertz image super-resolution

Haihang Ruan, Zhiyong Tan, Liangtao Chen, Wenjain Wan, and Juncheng Cao
Opt. Lett. 47(12) 3115-3118 (2022)

Hyperspectral image super-resolution via a multi-stage scheme without employing spatial degradation

Xuheng Cao, Yusheng Lian, Zilong Liu, Han Zhou, Bin Wang, Wan Zhang, and Beiqing Huang
Opt. Lett. 47(19) 5184-5187 (2022)

Lensfree dynamic super-resolved phase imaging based on active micro-scanning

Jialin Zhang, Qian Chen, Jiaji Li, Jiasong Sun, and Chao Zuo
Opt. Lett. 43(15) 3714-3717 (2018)

Rapid super-resolution line-scanning microscopy through virtually structured detection

Yanan Zhi, Rongwen Lu, Benquan Wang, Qiuxiang Zhang, and Xincheng Yao
Opt. Lett. 40(8) 1683-1686 (2015)