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Deep Unrolled Recovery in Sparse Biological Imaging
Yair Ben Sahel, John P. Bryan, Brian Cleary +2
Deep algorithm unrolling has emerged as a powerful model-based approach to develop deep architectures that combine the interpretability of iterative algorithms with the performance…
Deep Algorithm Unrolling for Biomedical Imaging
Yuelong Li, Or Bar-Shira, Vishal Monga +1
In this chapter, we review biomedical applications and breakthroughs via leveraging algorithm unrolling, an important technique that bridges between traditional iterative algorithm…
Learned super resolution ultrasound for improved breast lesion characterization
Or Bar-Shira, Ahuva Grubstein, Yael Rapson +5
Breast cancer is the most common malignancy in women. Mammographic findings such as microcalcifications and masses, as well as morphologic features of masses in sonographic scans,…
Image Restoration by Deep Projected GSURE
Shady Abu-Hussein, Tom Tirer, Se Young Chun +2
Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution. In recent years, solutions that are based on deep Convolutional Ne…
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation
Yair Kittenplon, Yonina C. Eldar, Dan Raviv
Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision. Traditional learning-based methods designed to learn end-to-end 3D flow of…
Unrolling of Deep Graph Total Variation for Image Denoising
Huy Vu, Gene Cheung, Yonina C. Eldar
While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely o…