On the use of deep learning for computational imaging
Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …
and machine learning have followed parallel tracks and, during the last two decades …
Mobile computational photography: A tour
The first mobile camera phone was sold only 20 years ago, when taking pictures with one's
phone was an oddity, and sharing pictures online was unheard of. Today, the smartphone is …
phone was an oddity, and sharing pictures online was unheard of. Today, the smartphone is …
Learning enriched features for real image restoration and enhancement
With the goal of recovering high-quality image content from its degraded version, image
restoration enjoys numerous applications, such as in surveillance, computational …
restoration enjoys numerous applications, such as in surveillance, computational …
Cycleisp: Real image restoration via improved data synthesis
The availability of large-scale datasets has helped unleash the true potential of deep
convolutional neural networks (CNNs). However, for the single-image denoising problem …
convolutional neural networks (CNNs). However, for the single-image denoising problem …
PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network
Partial differential equations (PDEs) are commonly derived based on empirical
observations. However, recent advances of technology enable us to collect and store …
observations. However, recent advances of technology enable us to collect and store …
Learning a single convolutional super-resolution network for multiple degradations
Recent years have witnessed the unprecedented success of deep convolutional neural
networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based …
networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based …
ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing
With the aim of developing a fast yet accurate algorithm for compressive sensing (CS)
reconstruction of natural images, we combine in this paper the merits of two existing …
reconstruction of natural images, we combine in this paper the merits of two existing …
Pde-net: Learning pdes from data
Partial differential equations (PDEs) play a prominent role in many disciplines of science
and engineering. PDEs are commonly derived based on empirical observations. However …
and engineering. PDEs are commonly derived based on empirical observations. However …
Learning a variational network for reconstruction of accelerated MRI data
Purpose To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR
data by learning a variational network that combines the mathematical structure of …
data by learning a variational network that combines the mathematical structure of …
Unprocessing images for learned raw denoising
Abstract Machine learning techniques work best when the data used for training resembles
the data used for evaluation. This holds true for learned single-image denoising algorithms …
the data used for evaluation. This holds true for learned single-image denoising algorithms …