Efficient image interpolation by associating 2nd order local structure and data-adaptive kernel regression

2012 ◽  
Author(s):  
Ryotaro Nakamura ◽  
Takayuki Nakachi ◽  
Nozomu Hamada
Keyword(s):  
Local Structure ◽  
Kernel Regression ◽  
Image Interpolation ◽  
Adaptive Kernel ◽  
Data Adaptive

Image De-Noising Based on Improved Data-Adaptive Kernel Regression Method

2012 ◽  
Vol 532-533 ◽  
pp. 1359-1364 ◽  
Author(s):  
Zhen Ping Qiang ◽  
Xu Chen ◽  
Hong Lin ◽  
Tong Lin Zhao
Keyword(s):  
High Frequency ◽  
Iterative Process ◽  
Kernel Regression ◽  
Regression Method ◽  
Frequency Noise ◽  
High Frequency Noise ◽  
Image Characteristics ◽  
Adaptive Kernel ◽  
Novel Method ◽  
Data Adaptive

This paper proposes a novel method for image de-noising, the algorithm is improved the data-adaptive kernel regression method. The process of each pixel is: first determine whether the pixel is on boundary, for the pixels on the edge to establish the kernel which shape is adaptive with the boundary, and then use iterative process for de-noising. For non-boundary pixels, use the data-adaptive iterative kernel regression method. Experiments have shown promising results in image de-noising; the algorithm is able to filter out the high-frequency noise of image while it retains the details of the image characteristics.

scholarly journals Fast Feature-Preserving Approach to Carpal Bone Surface Denoising

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2379
Author(s):  
Ibrahim Salim ◽  
A. Hamza
Keyword(s):  
Objective Function ◽  
Linear Equations ◽  
Noise Model ◽  
Carpal Bone ◽  
Geometric Framework ◽  
Sparse System ◽  
Adaptive Kernel ◽  
Feature Preserving ◽  
Data Adaptive ◽  
Matrix Balancing

We present a geometric framework for surface denoising using graph signal processing, which is an emerging field that aims to develop new tools for processing and analyzing graph-structured data. The proposed approach is formulated as a constrained optimization problem whose objective function consists of a fidelity term specified by a noise model and a regularization term associated with prior data. Both terms are weighted by a normalized mesh Laplacian, which is defined in terms of a data-adaptive kernel similarity matrix in conjunction with matrix balancing. Minimizing the objective function reduces it to iteratively solve a sparse system of linear equations via the conjugate gradient method. Extensive experiments on noisy carpal bone surfaces demonstrate the effectiveness of our approach in comparison with existing methods. We perform both qualitative and quantitative comparisons using various evaluation metrics.

scholarly journals Function Estimation Using Data-Adaptive Kernel Smoothers—How Much Smoothing?

1994 ◽  
Vol 8 (4) ◽  
pp. 402 ◽  
Author(s):  
K. S. Riedel ◽  
A. Sidorenko ◽  
James R. Matey
Keyword(s):  
Function Estimation ◽  
Adaptive Kernel ◽  
Data Adaptive ◽  
Using Data ◽  
Kernel Smoothers

AN IMPROVED EDGE-DIRECTED IMAGE INTERPOLATION ALGORITHM

2012 ◽  
Vol 12 (04) ◽  
pp. 1250023 ◽  
Author(s):  
XINGYUAN WANG ◽  
ZHIFENG CHEN ◽  
XUEMEI BAO
Keyword(s):  
Real Time ◽  
Local Structure ◽  
Time Complexity ◽  
Image Interpolation ◽  
Threshold Value ◽  
Subjective Quality ◽  
Experimental Results ◽  
Interpolation Algorithm ◽  
Visual Effects ◽  
Structure Characteristics

The paper sets forth an improved edge-directed image interpolation algorithm with low time complexity. The algorithm partitions images into homogeneous and edge areas by setting the preset threshold value based on the local structure characteristics. Specified algorithms are assigned to interpolate each classified areas respectively. The proposed method implements strategy in three steps to interpolate after setting the preset threshold value. In this way, it can achieve the goals of real-time interpolation and good subjective quality. Furthermore, the interpolated images have much more explicit edge regions and better visual effects using our proposed method than that of using other algorithms. Experimental results demonstrate that the method proposed by the authors is high-performed in image interpolation.

scholarly journals Joint-Saliency Structure Adaptive Kernel Regression with Adaptive-Scale Kernels for Deformable Registration of Challenging Images

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 330-343 ◽  
Author(s):  
Binjie Qin ◽  
Zhuangming Shen ◽  
Zeshan Fu ◽  
Zien Zhou ◽  
Yisong Lv ◽  
...  
Keyword(s):  
Kernel Regression ◽  
Deformable Registration ◽  
Adaptive Kernel

Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression

NeuroImage ◽  
2012 ◽  
Vol 59 (3) ◽  
pp. 2255-2265 ◽  
Author(s):  
Ahmed Serag ◽  
Paul Aljabar ◽  
Gareth Ball ◽  
Serena J. Counsell ◽  
James P. Boardman ◽  
...  
Keyword(s):  
Kernel Regression ◽  
Developing Brain ◽  
High Definition ◽  
Adaptive Kernel ◽  
Spatio Temporal

scholarly journals A locally adaptive kernel regression method for facies delineation

2015 ◽  
Vol 531 ◽  
pp. 62-72
Author(s):  
D. Fernàndez-Garcia ◽  
M. Barahona-Palomo ◽  
C.V. Henri ◽  
X. Sanchez-Vila
Keyword(s):  
Kernel Regression ◽  
Regression Method ◽  
Locally Adaptive ◽  
Adaptive Kernel

Structure matching driven by joint-saliency-structure adaptive kernel regression

2016 ◽  
Vol 46 ◽  
pp. 851-867 ◽  
Author(s):  
Binjie Qin ◽  
Zhuangming Shen ◽  
Zien Zhou ◽  
Jiawei Zhou ◽  
Yisong Lv
Keyword(s):  
Kernel Regression ◽  
Adaptive Kernel

scholarly journals Erratum to Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression [NeuroImage 59/3(2012) 2255–2265]

NeuroImage ◽  
2012 ◽  
Vol 63 (2) ◽  
pp. 998
Author(s):  
Ahmed Serag ◽  
Paul Aljabar ◽  
Gareth Ball ◽  
Serena J. Counsell ◽  
James P. Boardman ◽  
...  
Keyword(s):  
Kernel Regression ◽  
Developing Brain ◽  
High Definition ◽  
Adaptive Kernel ◽  
Spatio Temporal
Sign in / Sign up

Export Citation Format

Share Document