Medical imaging is fundamentally challenging due to absorption and scattering in tissues and by the need to minimize illumination of the patient with harmful radiation. Imaging modalities also suffer from low spatial resolution, limited dynamic range and low contrast. These predicaments have fueled interest in enhancing medical images using digital post processing. Recent progress in image super resolution using machine learning and in particular convolutional neural networks (CNNs) may offer new possibilities for improving the quality of medical images. However, the tendency of CNNs to hallucinate image details is detrimental for medical images as it may lead to false diagnostics. Also, these techniques require prohibitively large computational resource, a problem that is exacerbated by the large size of medical images. Rapid and Accurate Image Super Resolution (RAISR) method provides a computationally efficient solution for image upscaling. In this paper, we propose ARAISR, an improved variant of RAISR, which inherits the local features and regression model of RAISR but instead of utilizing cluster anchored points to represent image feature space. This algorithm combines the low computing complexity of RAISR with the feature enhancement advantage of phase stretch transform (PST), a new computational approach that is inspired by the physics of photonic time stretch technique. We obtain improved quality (i.e. maximum 1dB PSNR better than RAISR) and hallucination-free performance for medical images super resolution.
The cat-eye effect reflected beam profiles of most optical detectors have a certain characteristic of periodicity, which is caused by array arrangement of sensors at their optical focal planes. It is the first time to find and prove that the reflected beam profile becomes several periodic spots at the reflected propagation distance corresponding to half the imaging distance of a CCD camera. Furthermore, the spatial cycle of these spots is approximately constant, independent of the CCD camera’s imaging distance, which is related only to the focal length and pixel size of the CCD sensor. Thus, we can obtain the imaging distance and intrinsic parameters of the optical detector by analyzing its cat-eye reflected beam profiles. This conclusion can be applied in the field of non-cooperative cat-eye target recognition.
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.