Fully Synthetic Training for Image Restoration Tasks
نویسندگان
چکیده
منابع مشابه
Image formation and restoration using multi-element synthetic array processing
Traditionally, the number of transmit and receive processing channels is equal to the number of transducers (N) in an ultrasound imaging system. Certain applications limit the number of processing channels such that there are fewer channels than transducer elements. For these cases, a subset of M adjacent transducers—a multi-element subarray— performs echo transmission and reception. The proces...
متن کاملImage Restoration Using A PDE-Based Approach
Image restoration is an essential preprocessing step for many image analysis applications. In any image restoration techniques, keeping structure of the image unchanged is very important. Such structure in an image often corresponds to the region discontinuities and edges. The techniques based on partial differential equations, such as the heat equations, are receiving considerable attention i...
متن کاملGeneral restoration filter for vibrated-image restoration.
Mechanical vibrations are often the principal cause of image degradation. Low temporal-frequency mechanical vibrations involve random image degradation that depends on the instant of exposure. Exact restoration requires the calculation of a specific filter unique to each vibrated image. To calculate the restoration filter for each image, one needs the specific optical transfer function unique t...
متن کاملImproved Variance Estimation for Fully Synthetic Datasets
Fully synthetic datasets, i.e. datasets that only contain simulated values, arguably provide a very high level of data protection. Since all values are simulated reidentification is almost impossible. This makes the approach especially attractive for the release of very sensitive data such as medical records. However, the established variance estimate for fully synthetic datasets has two major ...
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: Social Science Research Network
سال: 2022
ISSN: ['1556-5068']
DOI: https://doi.org/10.2139/ssrn.4176695