Recent smartphones adapt the camera module with optical image stabilizer(OIS) to enhance imaging quality in handshaking conditions. However, compared to the non-OIS camera module, the cost for implementing the OIS module is still high. One reason is that the production line for the OIS camera module requires a highly precise shaker table in final test process, which increases the unit cost of the production. In this paper, we propose a framework for the OIS quality prediction that is trained with the support vector machine and following module characterizing features : noise spectral density of gyroscope, optically measured linearity and cross-axis movement of hall and actuator. The classifier was tested on an actual production line and resulted in 88% accuracy of recall rate.
We implement differential interference contrast (DIC) microscopy using high-speed synthetic aperture imaging that expands the passband of coherent imaging by a factor of 2.2. For an aperture synthesized coherent image, we apply for the numerical post-processing and obtain a high-contrast DIC image for arbitrary shearing direction and bias retardation. In addition, we obtain images at different depths without a scanning objective lens by numerically propagating the acquired coherent images. Our method achieves high-resolution and high-contrast 3-D DIC imaging of live biological cells. The proposed method will be useful for monitoring 3-D dynamics of intracellular particles.