Holographic optical field recovery using a regularized untrained deep decoder network
نویسندگان
چکیده
منابع مشابه
Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network
In this paper, we introduce a novel technique to recover the pen trajectory of offline characters which is a crucial step for handwritten character recognition. Generally, online acquisition approach has more advantage than its offline counterpart as the online technique keeps track of the pen movement. Hence, pen tip trajectory retrieval from offline text can bridge the gap between online and ...
متن کاملSingle Image Reflection Removal Using Deep Encoder-Decoder Network
Image of a scene captured through a piece of transparent and reflective material, such as glass, is often spoiled by a superimposed layer of reflection image. While separating the reflection from a familiar object in an image is mentally not difficult for humans, it is a challenging, ill-posed problem in computer vision. In this paper, we propose a novel deep convolutional encoder-decoder metho...
متن کاملExtended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery
reconstruction using deep learning based autofocusing and phase-recovery YICHEN WU, YAIR RIVENSON, YIBO ZHANG, ZHENSONG WEI, HARUN GÜNAYDIN, XING LIN, AYDOGAN OZCAN 1,2,3,4,* Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA Bioengineering Department, University of California, Los Angeles, California 90095, USA California NanoSystems In...
متن کاملAS/AC Network with Holographic Optical Switches
The holographic optical switches are three-dimensional devices, having the characteristics of flexibility and compactness. The procedure to realize a 4×4 active splitter and active combiner network with holographic optical switches is discussed in detail. After optimum design, the unique features of compactness and flexibility of holographic optical switches not only significantly saves space o...
متن کاملShakeout: A New Regularized Deep Neural Network Training Scheme
Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. The invention of effective training techniques largely contributes to this success. The so-called "Dropout" training scheme is one of the most powerful tool to reduce over-fitting. From the statistic point of view, Dropout works by implicitly imposing an L2 regularizer on the weights....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Scientific Reports
سال: 2021
ISSN: 2045-2322
DOI: 10.1038/s41598-021-90312-5