Infrared hyperspectral cameras are complicated in structure, require a large amount of imaging data, and the array detector is expensive, which is not conducive to the efficient imaging and real-time data analysis of objects in the infrared regime. Single pixel imaging offers a solution by employing compressed sampling. This paper describes the design and real time operation of a parallel single-pixel near infrared hyperspectral imaging system which can obtain a 128×128×256 spectral cube image with a spectral resolution of 4 nm within the range of 975 to 1736 nm. The compression rate and reconstructed image quality using different orderings of the Hadamard modulation basis, including the natural, cake cutting, and the Russian dolls sequences, are compared. A good reconstructed image of a dynamic object can be realized even with a compression sampling rate as low as 0.78% with the Russian doll sequence. The method proposed in this paper should have many potential applications due to its efficient sampling and high-speed reconstruction enabled by parallel spectral channel computation.
The assessment of gastrointestinal health through tongue image analysis is a significant aspect of traditional Chinese medicine. Utilizing computer vision technology for the analysis of tongue image features and disease diagnosis has emerged as a focal point in medical image processing research. However, the current integration of deep learning with traditional Chinese medicine remains relatively limited, particularly in the comprehensive exploration of tongue image features based on traditional Chinese medical diagnostic theories. In this study, a variety of deep learning models were employed to perform classification tasks on the presence of common tongue features such as thick coating, cracks, tooth marks, and the existence of gastric diseases. The deep learning models utilized include CNN, ResNet, AlexNet, and DenseNet. Subsequently, DenseNet was used as the reference model to evaluate the performance of pre-training with the three tongue image features for gastric disease classification. The training and validation were conducted on tongue image datasets collected and annotated at the Department of Traditional Chinese Medicine of Peking University Third Hospital. Experimental results demonstrate that DenseNet achieved an AUROC value of 0.9207 for certain tongue image features. Different networks exhibited favorable performance in metrics such as Accuracy, Precision, and Recall. Moreover, the injection of the three tongue image features as prior information significantly enhanced the model's accuracy in identifying gastric diseases. Our research validates the feasibility of deep learning in intelligent tongue image diagnosis, laying a foundation for the digitization and intelligence of traditional Chinese medicine tongue diagnosis.
The image quality of traditional computed ghost imaging is largely affected by the motion state of the target. When the target moves more than one pixel in the adjacent sampling interval, the image quality will decrease rapidly. Especially when the motion status of the target is unknown, high quality imaging will become difficult. To overcome the problem, we propose a simple and reliable method of imaging the moving target based on the reconstruction of Fourier Ghost Imaging (FGI) and phase compensation. The method uses the Fourier positioning patterns to estimate the displacement trajectory of the object followed by phase compensation of the spectrum. It combines the conjugate symmetry and sparsity properties of the Fourier spectrum to directly reconstruct the reasonable image of a moving object at low sampling rates, which saves time and computation. Simulation results show that the method does not require excessive priori knowledge and can achieve imaging of an object in different states of motion.
KEYWORDS: Signal detection, Sensors, Signal to noise ratio, Computational imaging, Speckle, Interference (communication), Denoising, Digital micromirror devices, Image quality, Modulation
Computational ghost imaging has great application prospect in the fields of national defense and biomedical because of its features of breaking the diffraction limit, being able to image under extremely weak background and harsh conditions. However, susceptible to the noise of the space environment, it has a low environmental signal-to-noise ratio. This paper propose an algorithm of interpolation computational ghost imaging(ICGI) in the field of computational ghost imaging. For the purpose of weakening the influence of dynamic interference in the ghost imaging, it insert specific patterns to a digital micromirror device in the original light-emphasis sequence, linearly estimate the change in noise by illuminating incoherent light, and correlate the optical signal of the object with the optical signal of the incoherent modulated light to calculate and reconstruct the image of the object. And This algorithm and the quality of image is improved by means of interpolating between different amounts of random patterns and inserting specific patterns of different complexity.
We propose and demonstrate a new correlation imaging method using a periodic light source array. The image of the object is reconstructed by exploiting the correlation between the total intensity of the beam interacting with the object and the precomputed intensity distribution patterns of the light source. The implementation of this experiment is quite simple and low-cost without the need for a beam splitter or spatial light modulator. Due to its single-pixel detection configuration, it should have great potential in many imaging applications.
KEYWORDS: Image analysis, Signal to noise ratio, Image processing, Imaging systems, Computer simulations, Digital image processing, Information theory, Image compression, Digital micromirror devices, Sensors
In this paper, an evaluation criterion based on image complexity is proposed in ghost imaging. According to the iterative performance of ghost imaging, characteristic factors of describing image complexity are introduced for seeking a new evaluation criterion to improve evaluation method. The proposed image complexity can be utilized to assess the iterative performance of different ghost imaging algorithms. The assessment results indicate that the proposed image complexity has a similar function with SNR, which is used to evaluate the iterative performance of ghost imaging. Compared with other existing evaluation methods, the obvious advantage is availability when the original image is unknown, and experiment results demonstrate that the new evaluation criterion is valid in ghost imaging.
KEYWORDS: Sensors, Infrared imaging, Signal to noise ratio, Digital micromirror devices, Infrared sensors, Detection and tracking algorithms, Spatial resolution, Imaging systems, Signal detection, Image retrieval
Traditional imaging are mostly based on the principle of lens imaging which is simple but the imaging result is heavily dependent on the quality of detector. It is usual to increase the detector array density or reduce the size of pixels to improve the imaging resolution, especially for infrared imaging. It will decrease the light flux causing the noise enhance relatively and add the cost on the contrary. Besides, there is a novel imaging technology called ghost imaging. We present a new infrared imaging method named computational ghost imaging only using a bucket detector without spatial resolution, which avoiding the allocation of flux on the pixel dimension as well as reducing the cost.
Rayleigh backscattering noise, which is one of the reasons that limit the sensitivity, has been deemed as noise in traditional resonant optic gyroscopes. However Rayleigh backscattering noise is one of the incentives of mode splitting phenomenon in high-Q resonators. Regarding the change of the resonance frequency of the resonator caused by the scattering signal as a measurement, we can use mode splitting to measure temperature, size of nanoparticle, etc. Light is confined by total internal reflection in whispering gallery mode (WGM) optical resonators, which is characterized by high-Q factors and small mode volumes. With regards to this, we propose a sensing mechanism based on mode splitting in high-Q WGM optical resonators. It is possible for us to measure the angular velocity of carrier according to the changes in the resonant frequencies of the two splitting modes. We propose the Miniature resonant optic gyroscope based on mode splitting (MROG-MS) with WGM resonators in the paper. Considering the Sagnac effect, mode splitting in high quality optical micro-resonators, and the rotation-induced impact on backscattering process, we modify the equations of motion that describe mode splitting, derive the explicit expression of angular rate versus the splitting amount, and verify the sensing mechanism by the simulation based on COMSOL. Furthermore, after monitoring the transmission spectra at different number of scattering particles, the simulation shows that mode splitting phenomenon resulted by single particle is more suitable for angular velocity measurement.
Previous research employed natural background fractal features to detect man-made target for imaging infrared terminal guidance missile. The method is some effective but the optical turbulence is overlooked, which will disturb natural background intrinsic fractal. When the missile flies in atmosphere, there exits turbulent flow over the IR windows which will degrade the optical wave-fronts. In this paper, a new method considering degraded wave-fronts features is proposed for man-made moving target detection in natural background. To pre-process the infrared image and obtain the area in which target may exit, the image is divided into blocks and each block fractal dimension is calculated and compared. Then optical flow of the block is calculated from successive images to determine the moving target. The method is more applicable to actual missile fly environment, simulation results show that it reduces the optical flow calculation complexity and can detect the target availably.
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