A two-stage framework for DIC image denoising and Gabor based GLCM feature extraction for pre-cancer diagnosis

2018 ◽  
Author(s):  
Sawon Pratiher ◽  
Sabyasachi Mukhopadhyay ◽  
Nirmalya Ghosh ◽  
Prasanta K. Panigrahi ◽  
Sukanya Mukherjee ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Image Denoising ◽  
Cancer Diagnosis ◽  
Two Stage

scholarly journals A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 319
Author(s):  
Yi Wang ◽  
Xiao Song ◽  
Guanghong Gong ◽  
Ni Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Denoising ◽  
Color Image ◽  
Rapid Development ◽  
Similarity Index ◽  
Structural Similarity ◽  
Convolutional Network ◽  
Scale Feature ◽  
Multi Scale

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

A New Extension of Locality Preserving Projections for Image Feature Extraction

2012 ◽  
Vol 241-244 ◽  
pp. 1715-1718
Author(s):  
Guo Hong Huang
Keyword(s):  
Feature Extraction ◽  
Local Information ◽  
Image Feature ◽  
Two Dimensional ◽  
Two Stage ◽  
Image Feature Extraction ◽  
Locality Preserving ◽  
Image Manifold ◽  
New Framework ◽  
Novel Algorithm

This paper proposes a novel algorithm for image feature extraction, namely, the two-directional two-dimensional locality preserving projection, ((2D)2LPP), which can find an embedding from two directions that not only preserves local information and detect the intrinsic image manifold structure, but also combines the both information between rows and those between columns simultaneously. We also combine the advantages of (2D)2LPP and LDA, and propose a new framework for feature extraction as two-stage: “(2D)2LPP+LDA.” The LDA step is performed to further reduce the dimension of feature matrix in the (2D)2LPP subspace. Experimental results on ORL face databases demonstrate the effectiveness of the proposed methods.

UNCORRELATED PROJECTION DISCRIMINANT ANALYSIS AND ITS APPLICATION TO FACE IMAGE FEATURE EXTRACTION

2003 ◽  
Vol 17 (08) ◽  
pp. 1325-1347 ◽  
Author(s):  
JIAN YANG ◽  
JING-YU YANG ◽  
ALEJANDRO F. FRANGI ◽  
DAVID ZHANG
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Face Image ◽  
Desirable Property ◽  
Experimental Results ◽  
Image Feature ◽  
Analysis Method ◽  
Two Stage ◽  
Image Feature Extraction ◽  
Projection Analysis

In this paper, a novel image projection analysis method (UIPDA) is first developed for image feature extraction. In contrast to Liu's projection discriminant method, UIPDA has the desirable property that the projected feature vectors are mutually uncorrelated. Also, a new LDA technique called EULDA is presented for further feature extraction. The proposed methods are tested on the ORL and the NUST603 face databases. The experimental results demonstrate that: (i) UIPDA is superior to Liu's projection discriminant method and more efficient than Eigenfaces and Fisherfaces; (ii) EULDA outperforms the existing PCA plus LDA strategy; (iii) UIPDA plus EULDA is a very effective two-stage strategy for image feature extraction.

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