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Found 7 papers, 3 papers with code
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging
However, the spatial resolution is significantly constrained in commercial microlens based LF cameras because of the inherent multiplexing of spatial and angular information.
Zero-Shot Single Image Restoration Through Controlled Perturbation of Koschmieder's Model
Our zero-shot network estimates the parameters of the Koschmieder's model, which describes the degradation in the input image, to perform image restoration.
Progressive Update Guided Interdependent Networks for Single Image Dehazing
The estimated parameters are then used to guide our dehazing module, where the estimates are progressively updated by novel convolutional networks.
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network
Furthermore, this paper proposes an approach to reduce the model's uncertainty for an input image, and it helps to defend the adversarial attacks on the image super-resolution model.
UltraCompression: Framework for High Density Compression of Ultrasound Volumes using Physics Modeling Deep Neural Networks
Ultrasound image compression by preserving speckle-based key information is a challenging task.
Jointly Learning Convolutional Representations to Compress Radiological Images and Classify Thoracic Diseases in the Compressed Domain
Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain.
Ranked #5 on
Pneumonia Detection
on ChestX-ray14
Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms
Early works on medical image compression date to the 1980's with the impetus on deployment of teleradiology systems for high-resolution digital X-ray detectors.
