Real-time polarization measurement based on spatially modulated polarimeter and deep learning

Weng, Jianyu; Gao, Chao; Lei, Bing

doi:10.1016/j.rinp.2023.106280

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…In the above Sections, we mainly introduced examples of combining DL and PI in RS and some specific visible-wavelength applications. However, combining PI and DL can also be successfully used in other fields, such as biomedical imaging and computer vision [301][302][303][304].…”

Section: Othersmentioning

confidence: 99%

Polarimetric Imaging via Deep Learning: A Review

Liu

et al. 2023

Remote Sensing

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Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the increasing amount of data and the rapid development of physical models, deep learning (DL) and its related technique have become an irreplaceable solution for solving various tasks and breaking the limitations of traditional methods. PI and DL have been combined successfully to provide brand-new solutions to many practical applications. This review briefly introduces PI and DL’s most relevant concepts and models. It then shows how DL has been applied for PI tasks, including image restoration, object detection, image fusion, scene classification, and resolution improvement. The review covers the state-of-the-art works combining PI with DL algorithms and recommends some potential future research directions. We hope that the present work will be helpful for researchers in the fields of both optical imaging and RS, and that it will stimulate more ideas in this exciting research field.

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Section: Othersmentioning

confidence: 99%

Polarimetric Imaging via Deep Learning: A Review

Liu

et al. 2023

Remote Sensing

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“…Zhao et al [25] designed SpikeSR-Net to super-resolve a high-resolution image sequence from the low-resolution binary spike streams. In particular, in reference [26], a Convolutional Neural Network (CNN) based on VGGNet architecture was trained to obtain the polarization states of light using a single shot of intensity image.…”

Section: Introductionmentioning

confidence: 99%

An Improved CNN for Polarization Direction Measurement

Han,

Liu,

Wang

et al. 2023

Electronics

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Spatially polarization modulation has been proven to be an efficient and simple method for polarization measurement. Since the polarization information is encoded in the intensity distribution of the modulated light, the task of polarization measurement can be treated as the image processing problem, while the pattern of the light is captured by a camera. However, classical image processing methods could not meet the increasing demand of practical applications due to their poor computational efficiency. To address this issue, in this paper, an improved Convolutional Neural Network is proposed to extract the Stokes parameters of the light from the irradiance image. In our algorithm, residual blocks are adopted and different layers are connected to ensure that the underlying features include more details of the image. Furthermore, refined residual block and Global Average Pooling are introduced to avoid overfitting issues and gradient vanishing problems. Finally, our algorithm is tested on massive synthetic and real data, while the mean square error (MSE) between the extracted values and the true values of the normalized Stokes parameters is counted. Compared to VGG and FAM, the experimental results demonstrate that our algorithm has outstanding performance.

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