Complex "zero-shot" super-resolution reconstruction algorithm for THz imaging.

To deal with a terahertz (THz) super-resolution (SR) algorithm based on a convolutional neural network (CNN) without standard training datasets, a complex "zero-shot" SR (CZSSR) reconstruction algorithm is proposed according to the internal image statistics with a five-layer complex CNN model. Instead of relying on pre-training, the proposed method is of sound self-adaptability. Compared with real ZSSR, the peak SNR of CZSSR rose by about 0.94 dB, MSE decreased by 0.042, and SSIM increased by about 40% for the SR result of the measured data. The results show that the CZSSR method can solve the low-resolution problem of a THz imaging system and the shortage of datasets in THz SR based on CNN. Therefore, this research is of great significance for application in the fields of medical imaging and non-destructive detection.

Terahertz image super-resolution based on a complex convolutional neural network.

Terahertz (THz) imaging has been applied successfully in numerous applications, from medical imaging to industrial non-destructive detection. However, low resolution has always been a problem due to its long wavelength. A convolution neural network (CNN) is quite effective at improving the resolution of images in optics, in which real numbers are manipulated corresponding to measured intensity. Compared to optics, it is quite feasible to gain both the amplitude and phase information in THz imaging. In this Letter, we have extended the CNN from a real number domain to a complex number domain based on the wave nature of THz light. To the best of our knowledge, this is the first time that such a complex convolution neural network (CCNN) has been shown to be successful in THz imaging. We have proved that resolution can be 0.4 times of the beam size via this approach, and half a wavelength resolution can be obtained easily. Compared to the CNN, the CCNN generates an extra 27.8% increase in terms of contrast, implying a better image. Phase information can be recovered well, which is impossible for the CNN. Although the network is trained by the MNIST dataset, it is quite powerful for image reconstruction. Again, the CCNN outperforms the CNN in terms of generalization capability. We believe such an approach can help to overcome the lower-resolution bottleneck in THz imaging, and it can release the requirement of critical optical components and extensive fine-tuning in systems. THz biomedical imaging, non-destructive testing (NDT), and a lot of imaging applications can benefit from this approach.

Single-Camera-Based Method for Step Length Symmetry Measurement in Unconstrained Elderly Home Monitoring.

OBJECTIVE: single-camera-based gait monitoring is unobtrusive, inexpensive, and easy-to-use to monitor daily gait of seniors in their homes. However, most studies require subjects to walk perpendicularly to camera's optical axis or along some specified routes, which limits its application in elderly home monitoring. To build unconstrained monitoring environments, we propose a method to measure step length symmetry ratio (a useful gait parameter representing gait symmetry without significant relationship with age) from unconstrained straight walking using a single camera, without strict restrictions on walking directions or routes. METHODS: according to projective geometry theory, we first develop a calculation formula of step length ratio for the case of unconstrained straight-line walking. Then, to adapt to general cases, we propose to modify noncollinear footprints, and accordingly provide general procedure for step length ratio extraction from unconstrained straight walking. RESULTS: Our method achieves a mean absolute percentage error (MAPE) of 1.9547% for 15 subjects' normal and abnormal side-view gaits, and also obtains satisfactory MAPEs for non-side-view gaits (2.4026% for 45°-view gaits and 3.9721% for 30°-view gaits). The performance is much better than a well-established monocular gait measurement system suitable only for side-view gaits with a MAPE of 3.5538%. CONCLUSION: Independently of walking directions, our method can accurately estimate step length ratios from unconstrained straight walking. SIGNIFICANCE: This demonstrates our method is applicable for elders' daily gait monitoring to provide valuable information for elderly health care, such as abnormal gait recognition, fall risk assessment, etc. OBJECTIVE: single-camera-based gait monitoring is unobtrusive, inexpensive, and easy-to-use to monitor daily gait of seniors in their homes. However, most studies require subjects to walk perpendicularly to camera's optical axis or along some specified routes, which limits its application in elderly home monitoring. To build unconstrained monitoring environments, we propose a method to measure step length symmetry ratio (a useful gait parameter representing gait symmetry without significant relationship with age) from unconstrained straight walking using a single camera, without strict restrictions on walking directions or routes. METHODS: according to projective geometry theory, we first develop a calculation formula of step length ratio for the case of unconstrained straight-line walking. Then, to adapt to general cases, we propose to modify noncollinear footprints, and accordingly provide general procedure for step length ratio extraction from unconstrained straight walking. RESULTS: Our method achieves a mean absolute percentage error (MAPE) of 1.9547% for 15 subjects' normal and abnormal side-view gaits, and also obtains satisfactory MAPEs for non-side-view gaits (2.4026% for 45°-view gaits and 3.9721% for 30°-view gaits). The performance is much better than a well-established monocular gait measurement system suitable only for side-view gaits with a MAPE of 3.5538%. CONCLUSION: Independently of walking directions, our method can accurately estimate step length ratios from unconstrained straight walking. SIGNIFICANCE: This demonstrates our method is applicable for elders' daily gait monitoring to provide valuable information for elderly health care, such as abnormal gait recognition, fall risk assessment, etc.

Background Subtraction Based on Three-Dimensional Discrete Wavelet Transform.

Background subtraction without a separate training phase has become a critical task, because a sufficiently long and clean training sequence is usually unavailable, and people generally thirst for immediate detection results from the first frame of a video. Without a training phase, we propose a background subtraction method based on three-dimensional (3D) discrete wavelet transform (DWT). Static backgrounds with few variations along the time axis are characterized by intensity temporal consistency in the 3D space-time domain and, hence, correspond to low-frequency components in the 3D frequency domain. Enlightened by this, we eliminate low-frequency components that correspond to static backgrounds using the 3D DWT in order to extract moving objects. Owing to the multiscale analysis property of the 3D DWT, the elimination of low-frequency components in sub-bands of the 3D DWT is equivalent to performing a pyramidal 3D filter. This 3D filter brings advantages to our method in reserving the inner parts of detected objects and reducing the ringing around object boundaries. Moreover, we make use of wavelet shrinkage to remove disturbance of intensity temporal consistency and introduce an adaptive threshold based on the entropy of the histogram to obtain optimal detection results. Experimental results show that our method works effectively in situations lacking training opportunities and outperforms several popular techniques.

Millimeter wave imaging system modeling: spatial frequency domain calculation versus spatial domain calculation.

Active millimeter wave imaging systems have become a promising candidate for indoor security applications and industrial inspection. However, there is a lack of simulation tools at the system level. We introduce and evaluate two modeling approaches that are applied to active millimeter wave imaging systems. The first approach originates in Fourier optics and concerns the calculation in the spatial frequency domain. The second approach is based on wave propagation and corresponds to calculation in the spatial domain. We compare the two approaches in the case of both rough and smooth objects and point out that the spatial frequency domain calculation may suffer from a large error in amplitude of 50% in the case of rough objects. The comparison demonstrates that the concepts of point-spread function and f-number should be applied with careful consideration in coherent millimeter wave imaging systems. In the case of indoor applications, the near-field effect should be considered, and this is included in the spatial domain calculation.