A sensor-data-based denoising framework for hyperspectral images

Improved Calibration of Eye-in-Hand Robotic Vision System Based on Binocular Sensor

Eye-in-hand robotic binocular sensor systems are indispensable equipment in the modern manufacturing industry. However, because of the intrinsic deficiencies of the binocular sensor, such as the circle of confusion and observed error, the accuracy of the calibration matrix between the binocular sensor and the robot end is likely to decline. These deficiencies cause low accuracy of the matrix calibrated by the traditional method. In order to address this, an improved calibration method for the eye-in-hand robotic vision system based on the binocular sensor is proposed. First, to improve the accuracy of data used for solving the calibration matrix, a circle of confusion rectification method is proposed, which rectifies the position of the pixel in images in order to make the detected geometric feature close to the real situation. Subsequently, a transformation error correction method with the strong geometric constraint of a standard multi-target reference calibrator is developed, which introduces the observed error to the calibration matrix updating model. Finally, the effectiveness of the proposed method is validated by a series of experiments. The results show that the distance error is reduced to 0.080 mm from 0.192 mm compared with the traditional calibration method. Moreover, the measurement accuracy of local reference points with updated calibration results from the field is superior to 0.056 mm.

Improving the performance of rapid lifetime determination for wide-field time-gated imaging in live cells

In biological research, rapid wide-field fluorescence lifetime imaging has become an important imaging tool. However, the biological samples with weak fluorescence signals and lower sensitivity often suffer from very low precision in lifetime determinations which restricts its widespread utilization in many bioimaging applications. To address this issue, a method is presented in this paper to substantially enhance the precision of rapid lifetime determination (RLD). It expedites the discrimination of fluorescence lifetimes, even for the weak signals coming from the cells, stained with long-lived biocompatible AIS/ZnS QDs. The proposed method works in two phases. The first phase deals with the systematic noise analysis based on the signal and contrast of the images in a time-gated imaging system, wherein acquiring the high-quality imaging data through optimization of hardware parameters improves the overall system performance. In the second phase, the chosen images are treated using total variation denoising method combined with the Max/Min filtering method for extracting the region of interest to reconstruct the intensity images for RLD. We performed several experiments on live cells to demonstrate the improvements in imaging performance by the systematic optimizations and data treatment. Obtained results demonstrated a great enhancement in signal-to-noise and contrast-to-noise ratios beside witnessing an obvious improvement in RLD for weak signals. This approach can be used not only to improve the quality of time-gated imaging data but also for efficient fluorescence lifetime imaging of live biological samples without compromising imaging speed and light exposure.

Low Rank Tensor Completion With Poisson Observations

Poisson observations for videos are important models in video processing and computer vision. In this paper, we study the third-order tensor completion problem with Poisson observations. The main aim is to recover a tensor based on a small number of its Poisson observation entries. A existing matrix-based method may be applied to this problem via the matricized version of the tensor. However, this method does not leverage on the global low-rankness of a tensor and may be substantially suboptimal. Our approach is to consider the maximum likelihood estimate of the Poisson distribution, and utilize the Kullback-Leibler divergence for the data-fitting term to measure the observations and the underlying tensor. Moreover, we propose to employ a transformed tensor nuclear norm ball constraint and a bounded constraint of each entry, where the transformed tensor nuclear norm is used to get a lower transformed multi-rank tensor with suitable unitary transformation matrices. We show that the upper bound of the error of the estimator of the proposed model is less than that of the existing matrix-based method. Also an information theoretic lower error bound is established. An alternating direction method of multipliers is developed to solve the resulting convex optimization model. Extensive numerical experiments on synthetic data and real-world datasets are presented to demonstrate the effectiveness of our proposed model compared with existing tensor completion methods.

A Combined Quantitative Evaluation Model for the Capability of Hyperspectral Imagery for Mineral Mapping

To analyze the influence factors of hyperspectral remote sensing data processing, and quantitatively evaluate the application capability of hyperspectral data, a combined evaluation model based on the physical process of imaging and statistical analysis was proposed. The normalized average distance between different classes of ground cover is selected as the evaluation index. The proposed model considers the influence factors of the full radiation transmission process and processing algorithms. First- and second-order statistical characteristics (mean and covariance) were applied to calculate the changes for the imaging process based on the radiation energy transfer. The statistical analysis was combined with the remote sensing process and the application performance, which consists of the imaging system parameters and imaging conditions, by building the imaging system and processing models. The season (solar zenith angle), sensor parameters (ground sampling distance, modulation transfer function, spectral resolution, spectral response function, and signal to noise ratio), and number of features were considered in order to analyze the influence factors of the application capability level. Simulated and real data collected by Hymap in the Dongtianshan area (Xinjiang Province, China), were used to estimate the proposed model’s performance in the application of mineral mapping. The predicted application capability of the proposed model is consistent with the theoretical analysis.

HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

Simulated altitude exposure assessment by hyperspectral imaging

Testing the human body’s reaction to hypoxia (including the one generated by high altitude) is important in aeronautic medicine. This paper presents a method of monitoring blood oxygenation during experimental hypoxia using hyperspectral imaging (HSI) and a spectral unmixing model based on a modified Beer–Lambert law. A total of 20 healthy volunteers (males) aged 25 to 60 years were included in this study. A line-scan HSI system was used to acquire images of the faces of the subjects. The method generated oxyhemoglobin and deoxyhemoglobin distribution maps from the foreheads of the subjects at 5 and 10 min of hypoxia and after recovery in a high oxygen breathing mixture. The method also generated oxygen saturation maps that were validated using pulse oximetry. An interesting pattern of desaturation on the forehead was discovered during the study, showing one of the advantages of using HSI for skin oxygenation monitoring in hypoxic conditions. This could bring new insight into the physiological response to high altitude and may become a step forward in air crew testing.