777 research outputs found
Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks
We present a semi-blind, spatially-variant deconvolution technique aimed at
optical microscopy that combines a local estimation step of the point spread
function (PSF) and deconvolution using a spatially variant, regularized
Richardson-Lucy algorithm. To find the local PSF map in a computationally
tractable way, we train a convolutional neural network to perform regression of
an optical parametric model on synthetically blurred image patches. We
deconvolved both synthetic and experimentally-acquired data, and achieved an
improvement of image SNR of 1.00 dB on average, compared to other deconvolution
algorithms.Comment: 2018/02/11: submitted to IEEE ICIP 2018 - 2018/05/04: accepted to
IEEE ICIP 201
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Joint Blind Motion Deblurring and Depth Estimation of Light Field
Removing camera motion blur from a single light field is a challenging task
since it is highly ill-posed inverse problem. The problem becomes even worse
when blur kernel varies spatially due to scene depth variation and high-order
camera motion. In this paper, we propose a novel algorithm to estimate all blur
model variables jointly, including latent sub-aperture image, camera motion,
and scene depth from the blurred 4D light field. Exploiting multi-view nature
of a light field relieves the inverse property of the optimization by utilizing
strong depth cues and multi-view blur observation. The proposed joint
estimation achieves high quality light field deblurring and depth estimation
simultaneously under arbitrary 6-DOF camera motion and unconstrained scene
depth. Intensive experiment on real and synthetic blurred light field confirms
that the proposed algorithm outperforms the state-of-the-art light field
deblurring and depth estimation methods
Learning Blind Motion Deblurring
As handheld video cameras are now commonplace and available in every
smartphone, images and videos can be recorded almost everywhere at anytime.
However, taking a quick shot frequently yields a blurry result due to unwanted
camera shake during recording or moving objects in the scene. Removing these
artifacts from the blurry recordings is a highly ill-posed problem as neither
the sharp image nor the motion blur kernel is known. Propagating information
between multiple consecutive blurry observations can help restore the desired
sharp image or video. Solutions for blind deconvolution based on neural
networks rely on a massive amount of ground-truth data which is hard to
acquire. In this work, we propose an efficient approach to produce a
significant amount of realistic training data and introduce a novel recurrent
network architecture to deblur frames taking temporal information into account,
which can efficiently handle arbitrary spatial and temporal input sizes. We
demonstrate the versatility of our approach in a comprehensive comparison on a
number of challening real-world examples.Comment: International Conference on Computer Vision (ICCV) (2017
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