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A progressive feature enhancement deep network for large-scale remote sensing image superresolution
The pursuit of superresolution (SR) with large upscaling factors, such as, for enhancing the
spatial resolution of low-resolution (LR) remote sensing images is a persistent and …
spatial resolution of low-resolution (LR) remote sensing images is a persistent and …
Online monitoring of flotation froth bubble-size distributions via multiscale deblurring and multistage jum** feature-fused full convolutional networks
This article proposes an online bubble size distribution (BSD) monitoring scheme by
incorporating a multiscale-deblurring full convolutional network (FulConNet)(MsD) and a …
incorporating a multiscale-deblurring full convolutional network (FulConNet)(MsD) and a …
Convexity and level sets for interval-valued fuzzy sets
Abstract Convexity is a deeply studied concept since it is very useful in many fields of
mathematics, like optimization. When we deal with imprecision, the convexity is required as …
mathematics, like optimization. When we deal with imprecision, the convexity is required as …
Adjusted non-local regression and directional smoothness for image restoration
Image restoration (IR) problems are very important in many low-level vision tasks. Due to
their ill-posed natures, image priors are widely used to regularize the solution spaces …
their ill-posed natures, image priors are widely used to regularize the solution spaces …
Codes for distributed finite alphabet matrix-vector multiplication
Recent work has developed coding theoretic approaches to add redundancy to distributed
matrix-vector multiplications with the goal of speeding up the computation by mitigating the …
matrix-vector multiplications with the goal of speeding up the computation by mitigating the …
Contrast enhancement of microscopy images using image phase information
Contrast enhancement is an important preprocessing step for the analysis of microscopy
images. The main aim of contrast enhancement techniques is to increase the visibility of the …
images. The main aim of contrast enhancement techniques is to increase the visibility of the …
Proximal activation of smooth functions in splitting algorithms for convex image recovery
Structured convex optimization problems typically involve a mix of smooth and nonsmooth
functions. The common practice is to activate the smooth functions via their gradient and the …
functions. The common practice is to activate the smooth functions via their gradient and the …
Blind image deblurring using row–column sparse representations
Blind image deblurring is a particularly challenging inverse problem where the blur kernel is
unknown and must be estimated en route to recover the deblurred image. The problem is of …
unknown and must be estimated en route to recover the deblurred image. The problem is of …
Co-difference based object tracking algorithm for infrared videos
This paper presents a novel infrared (IR) object tracking algorithm based on the co-
difference matrix. Extraction of co-difference features is similar to the well known covariance …
difference matrix. Extraction of co-difference features is similar to the well known covariance …
Real-time low-cost drift compensation for chemical sensors using a deep neural network with hadamard transform and additive layers
In this paper, we propose a computationally efficient deep learning framework to address the
issue of sensitivity drift compensation for chemical sensors. The framework estimates the …
issue of sensitivity drift compensation for chemical sensors. The framework estimates the …