3,428 research outputs found
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
Kernelized Back-Projection Networks for Blind Super Resolution
Since non-blind Super Resolution (SR) fails to super-resolve Low-Resolution
(LR) images degraded by arbitrary degradations, SR with the degradation model
is required. However, this paper reveals that non-blind SR that is trained
simply with various blur kernels exhibits comparable performance as those with
the degradation model for blind SR. This result motivates us to revisit
high-performance non-blind SR and extend it to blind SR with blur kernels. This
paper proposes two SR networks by integrating kernel estimation and SR branches
in an iterative end-to-end manner. In the first model, which is called the
Kernel Conditioned Back-Projection Network (KCBPN), the low-dimensional kernel
representations are estimated for conditioning the SR branch. In our second
model, the Kernelized BackProjection Network (KBPN), a raw kernel is estimated
and directly employed for modeling the image degradation. The estimated kernel
is employed not only for back-propagating its residual but also for
forward-propagating the residual to iterative stages. This forward-propagation
encourages these stages to learn a variety of different features in different
stages by focusing on pixels with large residuals in each stage. Experimental
results validate the effectiveness of our proposed networks for kernel
estimation and SR. We will release the code for this work.Comment: The first two authors contributed equally to this wor
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
DynaVSR: Dynamic Adaptive Blind Video Super-Resolution
Most conventional supervised super-resolution (SR) algorithms assume that
low-resolution (LR) data is obtained by downscaling high-resolution (HR) data
with a fixed known kernel, but such an assumption often does not hold in real
scenarios. Some recent blind SR algorithms have been proposed to estimate
different downscaling kernels for each input LR image. However, they suffer
from heavy computational overhead, making them infeasible for direct
application to videos. In this work, we present DynaVSR, a novel
meta-learning-based framework for real-world video SR that enables efficient
downscaling model estimation and adaptation to the current input. Specifically,
we train a multi-frame downscaling module with various types of synthetic blur
kernels, which is seamlessly combined with a video SR network for input-aware
adaptation. Experimental results show that DynaVSR consistently improves the
performance of the state-of-the-art video SR models by a large margin, with an
order of magnitude faster inference time compared to the existing blind SR
approaches
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