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4 March 2019 Medical image super-resolution using phase stretch anchored regression (Conference Presentation)
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Proceedings Volume 10937, Optical Data Science II; 109370E (2019)
Event: SPIE OPTO, 2019, San Francisco, California, United States
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sifeng He and Bahram Jalali "Medical image super-resolution using phase stretch anchored regression (Conference Presentation)", Proc. SPIE 10937, Optical Data Science II, 109370E (4 March 2019);

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