Deep image prior
Deep convolutional networks have become a popular tool for image generation and
restoration. Generally, their excellent performance is imputed to their ability to learn realistic …
restoration. Generally, their excellent performance is imputed to their ability to learn realistic …
Scale-recurrent network for deep image deblurring
In single image deblurring, the``coarse-to-fine''scheme, ie gradually restoring the sharp
image on different resolutions in a pyramid, is very successful in both traditional optimization …
image on different resolutions in a pyramid, is very successful in both traditional optimization …
Deep image deblurring: A survey
Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …
sharp image from a blurred input image. Advances in deep learning have led to significant …
“zero-shot” super-resolution using deep internal learning
Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance in the past
few years. However, being supervised, these SR methods are restricted to specific training …
few years. However, being supervised, these SR methods are restricted to specific training …
Deep image prior
V Lempitsky, A Vedaldi… - 2018 IEEE/CVF …, 2018 - ieeexplore.ieee.org
Deep convolutional networks have become a popular tool for image generation and
restoration. Generally, their excellent performance is imputed to their ability to learn realistic …
restoration. Generally, their excellent performance is imputed to their ability to learn realistic …
Mutual affine network for spatially variant kernel estimation in blind image super-resolution
Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially
invariant across the whole image. However, such an assumption is rarely applicable for real …
invariant across the whole image. However, such an assumption is rarely applicable for real …
Event-based fusion for motion deblurring with cross-modal attention
Traditional frame-based cameras inevitably suffer from motion blur due to long exposure
times. As a kind of bio-inspired camera, the event camera records the intensity changes in …
times. As a kind of bio-inspired camera, the event camera records the intensity changes in …
State-of-the-art approaches for image deconvolution problems, including modern deep learning architectures
M Makarkin, D Bratashov - Micromachines, 2021 - mdpi.com
In modern digital microscopy, deconvolution methods are widely used to eliminate a number
of image defects and increase resolution. In this review, we have divided these methods into …
of image defects and increase resolution. In this review, we have divided these methods into …
Multi-temporal recurrent neural networks for progressive non-uniform single image deblurring with incremental temporal training
Blind non-uniform image deblurring for severe blurs induced by large motions is still
challenging. Multi-scale (MS) approach has been widely used for deblurring that …
challenging. Multi-scale (MS) approach has been widely used for deblurring that …
Pyramid attention network for image restoration
Self-similarity refers to the image prior widely used in image restoration algorithms that small
but similar patterns tend to occur at different locations and scales. However, recent …
but similar patterns tend to occur at different locations and scales. However, recent …