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.
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