Mr. Shiqi Chen Profile

Shiqi Chen

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Proceedings Article | 24 May 2023 Paper

Varifocal lens based on fifth-order XY polynomial freeform surface

ZhiChao Ye, Jiapu Yan, Tingting Jiang, Shiqi Chen, Zhihai Xu, Qi Li, Huajun Feng, Yueting Chen

Proceedings Volume 12706, 1270604 (2023) https://doi.org/10.1117/12.2681797

KEYWORDS: Zoom lenses, Optical analysis, Lens design

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Optical system with zooming ability is essential for space exploration. In this paper, we proposed a new varifocal lens design based on the fifth-order X-Y polynomial free-form surface. It can change its focal power by the vertical deflection of the two free-form surfaces. We described the proposed lens’s modulation phase function and aberration based on the first-order optical analysis. Simulation experiments compared the proposed lens with the Alvarez lens regarding surface depth and zoom capability. We further investigate the correspondence between vertical deflection and optical power. The results show that the focal power of the varifocal lens based on the fifth-order X-Y polynomial free-form surface is proportional to the cube of the lateral shift distance, which has a stronger zoom ability than the Alvarez lens. It can achieve a wide range of power changes with a smaller lateral displacement, contributing to the zoom system’s further miniaturization.

Proceedings Article | 12 April 2023 Paper

DR-UNet: dynamic residual U-Net for blind correction of optical degradation

Jingwen Zhou, Shiqi Chen, Qi Li, Tongyue Li, Huajun Feng

Proceedings Volume 12565, 125651B (2023) https://doi.org/10.1117/12.2662187

KEYWORDS: Image restoration, Deep learning, Image processing, Point spread functions, Cameras

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Due to the size limitation of mobile devices, their optical design is difficult to reach the level of professional equipment. Corresponding restoration methods are then needed to compensate for the shortage. However, most of the models are still static, which leads to their limited representation ability of images. To tackle this problem, we propose a plug-and-play deformable residual block for efficiently sampling the spatially related features at different scales. Moreover, considering that the optical degradation is closely correlated with the field-of-view (FOV), we introduce a FOV attention block based on omni-dimensional dynamic convolution to integrate spatial features. On this basis, we further propose a novel optical degradation correction model called DR-UNet. It is constructed on an encoder-decoder structure to capture multiscale information, along with several context blocks. By correcting the optical degradation in images from coarse to fine, we finally obtain high-quality and degradation-free images. Extensive results demonstrate that our method can compete favorably with some state-of-the-art methods.

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