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Bayesian retinex underwater image enhancement
This paper develops a Bayesian retinex algorithm for enhancing single underwater image
with multiorder gradient priors of reflectance and illumination. First, a simple yet effective …
with multiorder gradient priors of reflectance and illumination. First, a simple yet effective …
Solving constrained total-variation image restoration and reconstruction problems via alternating direction methods
In this paper, we study alternating direction methods for solving constrained total-variation
image restoration and reconstruction problems. Alternating direction methods can be …
image restoration and reconstruction problems. Alternating direction methods can be …
Constrained total variation deblurring models and fast algorithms based on alternating direction method of multipliers
The total variation (TV) model is attractive in that it is able to preserve sharp attributes in
images. However, the restored images from TV-based methods do not usually stay in a …
images. However, the restored images from TV-based methods do not usually stay in a …
Survey on sparsity in geometric modeling and processing
Techniques from sparse representation have been successfully applied in many areas like
digital image processing, computer vision and pattern recognition in the past ten years …
digital image processing, computer vision and pattern recognition in the past ten years …
A fast algorithm for Euler's elastica model using augmented Lagrangian method
Minimization of functionals related to Euler's elastica energy has a wide range of
applications in computer vision and image processing. A high order nonlinear partial …
applications in computer vision and image processing. A high order nonlinear partial …
Develop then rival: A human vision-inspired framework for superimposed image decomposition
A single superimposed image containing two image views causes visual confusion for both
human vision and computer vision. Human vision needs a “develop-then-rival” process to …
human vision and computer vision. Human vision needs a “develop-then-rival” process to …
Accelerating ADMM for efficient simulation and optimization
The alternating direction method of multipliers (ADMM) is a popular approach for solving
optimization problems that are potentially non-smooth and with hard constraints. It has been …
optimization problems that are potentially non-smooth and with hard constraints. It has been …
Primal–dual methods for large-scale and distributed convex optimization and data analytics
The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given
“difficult”(constrained) problem via finding solutions of a sequence of “easier”(often …
“difficult”(constrained) problem via finding solutions of a sequence of “easier”(often …
Convex image denoising via non-convex regularization with parameter selection
We introduce a convex non-convex (CNC) denoising variational model for restoring images
corrupted by additive white Gaussian noise. We propose the use of parameterized non …
corrupted by additive white Gaussian noise. We propose the use of parameterized non …
Adaptive directional total-variation model for latent fingerprint segmentation
A new image decomposition scheme, called the adaptive directional total variation (ADTV)
model, is proposed to achieve effective segmentation and enhancement for latent fingerprint …
model, is proposed to achieve effective segmentation and enhancement for latent fingerprint …