1,734 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
Convolutional Dictionary Learning: Acceleration and Convergence
Convolutional dictionary learning (CDL or sparsifying CDL) has many
applications in image processing and computer vision. There has been growing
interest in developing efficient algorithms for CDL, mostly relying on the
augmented Lagrangian (AL) method or the variant alternating direction method of
multipliers (ADMM). When their parameters are properly tuned, AL methods have
shown fast convergence in CDL. However, the parameter tuning process is not
trivial due to its data dependence and, in practice, the convergence of AL
methods depends on the AL parameters for nonconvex CDL problems. To moderate
these problems, this paper proposes a new practically feasible and convergent
Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The
BPG-M-based CDL is investigated with different block updating schemes and
majorization matrix designs, and further accelerated by incorporating some
momentum coefficient formulas and restarting techniques. All of the methods
investigated incorporate a boundary artifacts removal (or, more generally,
sampling) operator in the learning model. Numerical experiments show that,
without needing any parameter tuning process, the proposed BPG-M approach
converges more stably to desirable solutions of lower objective values than the
existing state-of-the-art ADMM algorithm and its memory-efficient variant do.
Compared to the ADMM approaches, the BPG-M method using a multi-block updating
scheme is particularly useful in single-threaded CDL algorithm handling large
datasets, due to its lower memory requirement and no polynomial computational
complexity. Image denoising experiments show that, for relatively strong
additive white Gaussian noise, the filters learned by BPG-M-based CDL
outperform those trained by the ADMM approach.Comment: 21 pages, 7 figures, submitted to IEEE Transactions on Image
Processin
Recommended from our members
Efficient Debanding Filtering for Inverse Tone Mapped High Dynamic Range Videos
- …