To solve the problem of low resolution of underwater polarization imaging images, a method to improve the resolution of active underwater polarization imaging images is proposed, which includes an optimal orthogonal polarization image group acquisition method, and an accurate estimation and optimization method of the polarization degree of backscattered light and target reflected light. An experiment is carried out to improve the resolution of underwater image restoration based on linear polarized light active illumination. The experimental results show that the image group with the lowest correlation degree is used as the orthogonal polarization image group among multiple groups of orthogonal polarization images, and the scattering depolarization coefficient is used to accurately estimate and optimize the polarization degree of backscattered light and target reflected light, and the final restored target image has higher resolution. Compared with the existing image restoration algorithms, it has obvious advantages and is suitable for water media with different material objects and different turbidity.
KEYWORDS: Feature extraction, Image sharpness, Point spread functions, Education and training, Image processing, Neural networks, Data modeling, Visual process modeling, Microscopes, Head
Autofocus plays an important role in microscopic imaging. As an extension of image-based methods, learning-based methods make real-time autofocus possible. The recently proposed learning-based autofocus methods achieved promising results in estimating defocus distance. However, the focusing accuracy depends partly on the feature extraction ability of the network model, and what features are specifically extracted by the network contributed to its success remains a mystery. In this paper, a single-shot microscopic autofocus method was proposed, which predicts the defocus distance from a single natural image, to improve the model's ability to extract image detail features. Furthermore, we validate that the neural network model mainly predicts the defocus distance by focusing on the sharpness of texture and edge features, and visualize the weight of the predicting results. A realistic dataset of sufficient size was made to train all models. The experiment shows the proposed network model has better focusing accuracy compared with other models, with a mean focusing error of 0.44μm, and pays more attention to the texture and edge features.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.