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View article: Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey
Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey Open
Event camera sensors are bio-inspired sensors which asynchronously capture per-pixel brightness changes and output a stream of events encoding the polarity, location and time of these changes. These systems are witnessing rapid advancement…
View article: Artificially Generated Visual Scanpath Improves Multilabel Thoracic Disease Classification in Chest X-Ray Images
Artificially Generated Visual Scanpath Improves Multilabel Thoracic Disease Classification in Chest X-Ray Images Open
Expert radiologists visually scan Chest X-Ray (CXR) images, sequentially fixating on anatomical structures to perform disease diagnosis. An automatic multi-label classifier of diseases in CXR images can benefit by incorporating aspects of …
View article: Self-Supervision via Controlled Transformation and Unpaired Self-Conditioning for Low-Light Image Enhancement
Self-Supervision via Controlled Transformation and Unpaired Self-Conditioning for Low-Light Image Enhancement Open
Real-world low-light images captured by imaging devices suffer from poor visibility and require a domain-specific enhancement to produce artifact-free outputs that reveal details. In this paper, we propose an unpaired low-light image enhan…
View article: Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging Open
With the availability of commercial Light Field (LF) cameras, LF imaging has emerged as an up and coming technology in computational photography. However, the spatial resolution is significantly constrained in commercial microlens based LF…
View article: Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network Open
Convolutional neural network (CNN) has achieved unprecedented success in image super-resolution tasks in recent years. However, the network's performance depends on the distribution of the training sets and degrades on out-of-distribution …
View article: Transmission Map and Atmospheric Light Guided Iterative Updater Network for Single Image Dehazing
Transmission Map and Atmospheric Light Guided Iterative Updater Network for Single Image Dehazing Open
Hazy images obscure content visibility and hinder several subsequent computer vision tasks. For dehazing in a wide variety of hazy conditions, an end-to-end deep network jointly estimating the dehazed image along with suitable transmission…
View article: Progressive Update Guided Interdependent Networks for Single Image Dehazing
Progressive Update Guided Interdependent Networks for Single Image Dehazing Open
Images with haze of different varieties often pose a significant challenge to dehazing. Therefore, guidance by estimates of haze parameters related to the variety would be beneficial, and their progressive update jointly with haze reductio…
View article: Ultracompression: Framework For High Density Compression Of Ultrasound Volumes Using Physics Modeling Deep Neural Networks
Ultracompression: Framework For High Density Compression Of Ultrasound Volumes Using Physics Modeling Deep Neural Networks Open
Ultrasound image compression by preserving speckle-based key information is a challenging task. In this paper, we introduce an ultrasound image compression framework with the ability to retain realism of speckle appearance despite achievin…
View article: Fast Bayesian Uncertainty Estimation of Batch Normalized Single Image Super-Resolution Network.
Fast Bayesian Uncertainty Estimation of Batch Normalized Single Image Super-Resolution Network. Open
In recent years, deep convolutional neural network (CNN) has achieved unprecedented success in image super-resolution (SR) task. But the black-box nature of the neural network and due to its lack of transparency, it is hard to trust the ou…
View article: Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized\n Single Image Super-Resolution Network
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized\n Single Image Super-Resolution Network Open
Convolutional neural network (CNN) has achieved unprecedented success in\nimage super-resolution tasks in recent years. However, the network's\nperformance depends on the distribution of the training sets and degrades on\nout-of-distributi…
View article: Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms
Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms Open
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. Commercially deployed systems during the period could compress 4,096 x 4,096 s…
View article: Fully Convolutional Model for Variable Bit Length and Lossy High Density\n Compression of Mammograms
Fully Convolutional Model for Variable Bit Length and Lossy High Density\n Compression of Mammograms Open
Early works on medical image compression date to the 1980's with the impetus\non deployment of teleradiology systems for high-resolution digital X-ray\ndetectors. Commercially deployed systems during the period could compress 4,096\nx 4,09…