Yeast cells are always used in the biofuel, alcoholic and baking industries. Real-time monitoring of the yeast concentration without any algorithm processing is significant to master the yeast cells surviving condition. Here we demonstrate a new method to monitor the yeast cell concentration using our previous developed microscope—portable microscopy based on fiber-optic array (FOA). The sample is illuminated by a broadband LED and a light diffuser is used to create uniform illumination over the entire field-of-view (FOV). The image pre-processing including the denoising and binarization are used for the images optimization to increase the signal-to-noise ratio (SNR). Automated counting of the yeast cells is performed on a computer using ImageJ. The paper provides a new method for the counting of the yeast cells.
The optical fiber taper coupled with CMOS has advantages of high sensitivity, compact structure and low distortion in the imaging platform. So it is widely used in low light, high speed and X-ray imaging systems. In the meanwhile, the peculiarity of the coupled structure can meet the needs of the demand in microscopy imaging. Toward this end, we developed a microscopic imaging platform based on the coupling of cellphone camera module and fiber optic taper for the measurement of the human blood samples and ascaris lumbricoides. The platform, weighing 70 grams, is based on the existing camera module of the smartphone and a fiber-optic array which providing a magnification factor of ~6x.The top facet of the taper, on which samples are placed, serves as an irregular sampling grid for contact imaging. The magnified images of the sample, located on the bottom facet of the fiber, are then projected onto the CMOS sensor. This paper introduces the portable medical imaging system based on the optical fiber coupling with CMOS, and theoretically analyzes the feasibility of the system. The image data and process results either can be stored on the memory or transmitted to the remote medical institutions for the telemedicine. We validate the performance of this cell-phone based microscopy platform using human blood samples and test target, achieving comparable results to a standard bench-top microscope.
According to the situation of oil leakage and the development of oil detection technology, a wireless monitoring system, combining with the sensor technology, optical measurement technology, and wireless technology, is designed. In this paper, the architecture of a wireless system is designed. In the hardware, the collected data, acquired by photoelectric conversion and analog to digital conversion equipment, will be sent to the upper machine where they are saved and analyzed. The experimental results reveals that the wireless system has the characteristics of higher precision, more real-time and more convenient installation, it can reflect the condition of the measuring object truly and implement the dynamic monitoring for a long time on-site, stability—thus it has a good application prospect in the oil monitoring filed.
Light microscopy can not only address various diagnosis needs such as aquatic parasites and bacteria such as E. coli in water, but also provide a method for the screening of red tide. Traditional microscope based on the smartphone created by adding lens couldn’t keep the tradeoff between field-of-view(FOV) and the resolution. In this paper, we demonstrate a non-contact, light and cost-effective microscope platform, that can image highly dense samples with a spatial resolution of ~0.8um over a field-of-view(FOV) of >1mm2. After captured the direct images, we performed the pixel super-resolution algorithm to improve the image resolution and overcome the hardware interference. The system would be a good point-of-care diagnostic solution in resource limited settings. We validated the performance of the system by imaging resolution test targets, the squamous cell cancer(SqCC) and green algae that necessary to detect the squamous carcinoma and red tide
Deblurring images captured from low-illumination conditions is a challenging task, because these images contain few useful structures for kernel estimation. However, these images usually contain some light streaks, which are beneficial for estimating the blur kernel. One of our key observations is that these light streaks can provide a good initial value for a nonconvex problem in kernel estimation. The other one is that they record the track of the blur kernel at the moment when images are taken. Therefore, we propose a new prior for kernel estimation based on light streaks in this paper. Moreover, in order to ensure the shape of the blur kernel to be similar to that of light streaks during the updating, a new method is proposed to refine the shape of light streaks. With the help of the refined shape, our kernel estimation process does not require heuristic coarse-to-fine strategy, which is widely used in image deblurring methods. Quantitative experimental results show the effectiveness of the proposed method. In addition, we also demonstrate that the proposed method can be applied to the existing deblurring methods to achieve better performance.