723 research outputs found
Towards Interpretable Video Super-Resolution via Alternating Optimization
In this paper, we study a practical space-time video super-resolution (STVSR)
problem which aims at generating a high-framerate high-resolution sharp video
from a low-framerate low-resolution blurry video. Such problem often occurs
when recording a fast dynamic event with a low-framerate and low-resolution
camera, and the captured video would suffer from three typical issues: i)
motion blur occurs due to object/camera motions during exposure time; ii)
motion aliasing is unavoidable when the event temporal frequency exceeds the
Nyquist limit of temporal sampling; iii) high-frequency details are lost
because of the low spatial sampling rate. These issues can be alleviated by a
cascade of three separate sub-tasks, including video deblurring, frame
interpolation, and super-resolution, which, however, would fail to capture the
spatial and temporal correlations among video sequences. To address this, we
propose an interpretable STVSR framework by leveraging both model-based and
learning-based methods. Specifically, we formulate STVSR as a joint video
deblurring, frame interpolation, and super-resolution problem, and solve it as
two sub-problems in an alternate way. For the first sub-problem, we derive an
interpretable analytical solution and use it as a Fourier data transform layer.
Then, we propose a recurrent video enhancement layer for the second sub-problem
to further recover high-frequency details. Extensive experiments demonstrate
the superiority of our method in terms of quantitative metrics and visual
quality.Comment: ECCV 202
Formation, Simulation and Restoration of Hypertelescopes Images
This book is a collection of 19 articles which reflect the courses given at the Collège de France/Summer school “Reconstruction d'images − Applications astrophysiques“ held in Nice and Fréjus, France, from June 18 to 22, 2012. The articles presented in this volume address emerging concepts and methods that are useful in the complex process of improving our knowledge of the celestial objects, including Earth
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
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