We propose and demonstrate a new phase retrieval method based on a deep neural network (DNN) structure. By inputting only one sample interferogram, measured from an off-axis holography based quantitative phase microscope (QPM), the DNN can output an accurate quantitative phase image of the sample without using a calibration interferogram, therefore significantly simplifying the measurements procedure. Importantly, our method can eliminate the need of performing phase unwrapping, therefore making it easy to achieve real-time phase retrieval in different program platforms. We used different types of cells as test samples to characterize the performance of our method, and we found that the accuracy of our DNNbased phase retrieval method is similar compared with the standard Fourier transform based phase method, while the background phase noise is reduced. Considering the experimental procedures and image processing steps are significantly simplified, we envision this new phase retrieval method will make QPM more easily accessible in bioimaging and material metrology applications in the future.
According to the sequence of license plate image has low resolution, in order to improve the image resolution, this paper proposes a multiframe super-resolution image reconstruction algorithm based on overcome registration error and Gauss weight interpolation. First, multiframe image sub-pixel image registration, through extracting the SIFT keypoints of the image, rough matching with BBF algorithm, accurate matching with RANSAC algorithm, establishing the homography matrix between matching points. In order to overcome the registration error, this paper proposes a method based on threshold to overcome the registration error. Finally, the multiframe low resolution image is projected into the high resolution image grid by image registration. The value of the high resolution grid is reconstructed with a non-uniform interpolation algorithm based on Gauss weight. The experiments show that both on the subjective evaluation and objective evaluation can obtain satisfactory reconstruction results, and the registration error is robust.