210 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
Depth and IMU aided image deblurring based on deep learning
Abstract. With the wide usage and spread of camera phones, it becomes necessary to tackle the problem of the image blur. Embedding a camera in those small devices implies obviously small sensor size compared to sensors in professional cameras such as full-frame Digital Single-Lens Reflex (DSLR) cameras. As a result, this can dramatically affect the collected amount of photons on the image sensor. To overcome this, a long exposure time is needed, but with slight motions that often happen in handheld devices, experiencing image blur is inevitable. Our interest in this thesis is the motion blur that can be caused by the camera motion, scene (objects in the scene) motion, or generally the relative motion between the camera and scene. We use deep neural network (DNN) models in contrary to conventional (non DNN-based) methods which are computationally expensive and time-consuming. The process of deblurring an image is guided by utilizing the scene depth and camera’s inertial measurement unit (IMU) records. One of the challenges of adopting DNN solutions is that a relatively huge amount of data is needed to train the neural network. Moreover, several hyperparameters need to be tuned including the network architecture itself.
To train our network, a novel and promising method of synthesizing spatially-variant motion blur is proposed that considers the depth variations in the scene, which showed improvement of results against other methods. In addition to the synthetic dataset generation algorithm, a real blurry and sharp dataset collection setup is designed. This setup can provide thousands of real blurry and sharp images which can be of paramount benefit in DNN training or fine-tuning
Quantifying Uncertainty in High Dimensional Inverse Problems by Convex Optimisation
Inverse problems play a key role in modern image/signal processing methods.
However, since they are generally ill-conditioned or ill-posed due to lack of
observations, their solutions may have significant intrinsic uncertainty.
Analysing and quantifying this uncertainty is very challenging, particularly in
high-dimensional problems and problems with non-smooth objective functionals
(e.g. sparsity-promoting priors). In this article, a series of strategies to
visualise this uncertainty are presented, e.g. highest posterior density
credible regions, and local credible intervals (cf. error bars) for individual
pixels and superpixels. Our methods support non-smooth priors for inverse
problems and can be scaled to high-dimensional settings. Moreover, we present
strategies to automatically set regularisation parameters so that the proposed
uncertainty quantification (UQ) strategies become much easier to use. Also,
different kinds of dictionaries (complete and over-complete) are used to
represent the image/signal and their performance in the proposed UQ methodology
is investigated.Comment: 5 pages, 5 figure
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