Rethinking coarse-to-fine approach in single image deblurring
Coarse-to-fine strategies have been extensively used for the architecture design of single
image deblurring networks. Conventional methods typically stack sub-networks with multi …
image deblurring networks. Conventional methods typically stack sub-networks with multi …
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 …
Singan: Learning a generative model from a single natural image
We introduce SinGAN, an unconditional generative model that can be learned from a single
natural image. Our model is trained to capture the internal distribution of patches within the …
natural image. Our model is trained to capture the internal distribution of patches within the …
“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 …
Single-frame super-resolution in remote sensing: A practical overview
Image acquisition technology is improving very fast from a performance point of view.
However, there are physical restrictions that can only be solved using software processing …
However, there are physical restrictions that can only be solved using software processing …
Neural blind deconvolution using deep priors
Blind deconvolution is a classical yet challenging low-level vision problem with many real-
world applications. Traditional maximum a posterior (MAP) based methods rely heavily on …
world applications. Traditional maximum a posterior (MAP) based methods rely heavily on …
Human-aware motion deblurring
This paper proposes a human-aware deblurring model that disentangles the motion blur
between foreground (FG) humans and background (BG). The proposed model is based on a …
between foreground (FG) humans and background (BG). The proposed model is based on a …
Deep video deblurring for hand-held cameras
Motion blur from camera shake is a major problem in videos captured by hand-held devices.
Unlike single-image deblurring, video-based approaches can take advantage of the …
Unlike single-image deblurring, video-based approaches can take advantage of the …
Blind image deblurring using dark channel prior
We present a simple and effective blind image deblurring method based on the dark
channel prior. Our work is inspired by the interesting observation that the dark channel of …
channel prior. Our work is inspired by the interesting observation that the dark channel of …
Learning a convolutional neural network for non-uniform motion blur removal
In this paper, we address the problem of estimating and removing non-uniform motion blur
from a single blurry image. We propose a deep learning approach to predicting the …
from a single blurry image. We propose a deep learning approach to predicting the …