Targeting on Enhancing 2D Aerial Image Information by Technology of Unsharp Masking

2013 ◽  
Vol 333-335 ◽  
pp. 836-838
Author(s):  
Chang Hai Li ◽  
Jun Zhang ◽  
Yan Chun Liu ◽  
Zhi Hui Sun
Keyword(s):  
Wavelet Decomposition ◽  
Low Frequency ◽  
Window Size ◽  
Texture Features ◽  
Aerial Image ◽  
Enhancement Effect ◽  
Original Graph ◽  
Unsharp Masking ◽  
Traditional Algorithm ◽  
Wavelet Transforming

To obtain image enhancement by getting detail texture features rich through the subtraction between the original graph and low-frequency graph, utilizing the wavelet decomposition technique, separating the components of high frequency and low frequency in the 2D (two-dimensional) aerial image, filtering the low frequency subgraph, and retaining only the low-frequency part. Practice has proved that the unsharp masking algorithm of wavelet transforming has obvious superiority over the traditional algorithm. The algorithm can separate characteristics of different resolution details, adjust the filter window size, make the wavelet components of different scales enhanced, the detail more clear, get a strong sense of layer and good enhancement effect.

A New Preprocessing Algorithm of Hand Vein Image

2013 ◽  
Vol 462-463 ◽  
pp. 312-315
Author(s):  
Cai Xia Liu
Keyword(s):  
Wavelet Decomposition ◽  
Feature Recognition ◽  
Hot Spot ◽  
Low Frequency ◽  
Histogram Equalization ◽  
Segmentation Algorithm ◽  
Human Hand ◽  
Security Technology ◽  
Hand Vein ◽  
Vein Recognition

Biometrics technology is an important security technology and the research of it has become a new hot spot for its superior security features. Then hand vein recognition is a new biological feature recognition which has many advantages, such as safety, non-contact. According to the features of human hand vein image, a hand vein preprocessing method based on wavelet transform and windows maximum between-class difference method threshold (OTSU) segmentation algorithm is proposed. In this paper, the hand vein image is enhanced by adaptive histogram equalization in low frequency part of the hand vein image after wavelet decomposition and filtering before feature extraction. Then the windows OTSU threshold segmentation algorithm is used to get the features. The experimental results show that this method is simple and easy to realize and has laid a good foundation for the latter part of the vein recognition.

A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain

2016 ◽  
Vol 39 (2) ◽  
pp. 183-193 ◽  
Author(s):  
Lu Liu ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Contourlet Transform ◽  
Inverse Transformation ◽  
Unsharp Masking ◽  
Mean Filter ◽  
Enhancement Method

The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhancement. A new remote sensing image enhancement method using mean filter and unsharp masking methods based on non-subsampled contourlet transform (NSCT) in the scope for greyscale images is proposed in this paper. First, the initial image is decomposed into the NSCT domain with a low-frequency sub-band and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. The mean filter is used for the coefficients of the first high-frequency sub-band. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The experimental results show that the proposed method is superior to other methods in improving image definition, image contrast and enhancing image edges.

scholarly journals Cotton stubble detection based on wavelet decomposition and texture features

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Yukun Yang ◽  
Jing Nie ◽  
Za Kan ◽  
Shuo Yang ◽  
Hangxing Zhao ◽  
...  
Keyword(s):  
Navigation System ◽  
Wavelet Decomposition ◽  
Back Propagation ◽  
Texture Feature ◽  
Texture Features ◽  
Back Propagation Neural Network ◽  
Visual Navigation ◽  
Image Texture ◽  
Original Image ◽  
Residual Film

Abstract Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.

scholarly journals Adaptive Enhancement Algorithm of High-Resolution Satellite Image Based on Feature Fusion

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Ruizhe Wang ◽  
Wang Xiao
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Feature Fusion ◽  
Satellite Image ◽  
Texture Features ◽  
Noise Removal ◽  
Enhancement Effect ◽  
Adaptive Enhancement ◽  
High Resolution Satellite Images ◽  
Enhancement Algorithm

Since the traditional adaptive enhancement algorithm of high-resolution satellite images has the problems of poor enhancement effect and long enhancement time, an adaptive enhancement algorithm of high-resolution satellite images based on feature fusion is proposed. The noise removal and quality enhancement areas of high-resolution satellite images are determined by collecting a priori information. On this basis, the histogram is used to equalize the high-resolution satellite images, and the local texture features of the images are extracted in combination with the local variance theory. According to the extracted features, the illumination components are estimated by Gaussian low-pass filtering. The illumination components are fused to complete the adaptive enhancement of high-resolution satellite images. Simulation results show that the proposed algorithm has a better adaptive enhancement effect, higher image definition, and shorter enhancement time.

The Influence of Widow Size on Remote Sensing-Based Prediction of Forest Structural Variables

2021 ◽  
Author(s):  
Ulaş Yunus ÖZKAN ◽  
Tufan Demirel
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Window Size ◽  
Aerial Image ◽  
Lidar Data ◽  
Black Pine ◽  
Sensing Data ◽  
Structural Variables ◽  
Pine Stands ◽  
Optimal Window

Abstract Background: Determining the appropriate window size is a critical step for estimating stand structural variables based on remote sensing data. Because the value of the reference laser and image metrics that affect the quality of the prediction model depends on window size. However, suitable window sizes are usually determined by trial and error. There are a limited number of published studies evaluating appropriate window sizes for different remote sensing data. This research investigated the effect of window size on predicting forest structural variables using airborne LiDAR data, digital aerial image and WorldView-3 satellite image.Results: In the WorldView-3 and digital aerial image, significant differences were observed in the prediction accuracies of the structural variables according to different window sizes. For the estimation based on WorldView-3 in black pine stands, the optimal window sizes for stem number (N), volume (V), basal area (BA) and mean height (H) were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. In oak stands, the R2 values of each moving window size were almost identical for N and BA. The optimal window size was 400 m2 for V and 600 m2 for H. For the estimation based on aerial image in black pine stands, the 800 m2 window size is optimal for N and H, the 600 m2 window size is optimal for V and the 1000 m2 window size is optimal for BA. In the oak stands, the optimal window sizes for N, V, BA and H were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. The optimal window sizes may need to be scaled up or down to match the stand canopy components. In the LiDAR data, the R2 values of each window size were almost identical for all variables of the black pine and the oak stands.Conclusion: This study illustrated that the window size has an effect on the prediction accuracy in estimating forest structural variables based on remote sensing data. Moreover, the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.

Spectrum Classification of Easily Confused Ground Objects in ALI Remote Sensing Image Based on Texture Features

2012 ◽  
Vol 610-613 ◽  
pp. 3606-3611
Author(s):  
Ling Ling Zhang ◽  
Ge Ying Lai ◽  
Xiang Gui Zeng ◽  
Fa Zhao Yi
Keyword(s):  
Maximum Likelihood Method ◽  
Research Result ◽  
Principal Component ◽  
Window Size ◽  
Texture Features ◽  
Likelihood Method ◽  
Forest Land ◽  
Classification Result ◽  
Occurrence Matrix

According to the problem that the classification result of shrub and forest land was easy to confuse when used spectrum of Advanced Land Image (ALI) to classify. This paper used the Meijiang River watershed as the study area. Used the Principal Component Analysis (PCA) to reduce dimension, taken the Contrast, Second moment, Mean and Dissimilarity as the texture values, and extracted the texture by Gray level co-occurrence matrix (GLCM). The texture features extracted from different window sizes were used the Maximum likelihood method to classify, and chosen the texture features extracted by the most suitable window size to join the classification. The research result shows that the texture features extracted by window size of 11×11 can distinguish well the two easily ground objects; moreover, the overall accuracy of classification used texture and spectrum features reached to 87.55%, which is 4.4% higher than the classification with spectrum.

scholarly journals IMAGE RECONSTRUCTION BASED ON COMBINATION OF WAVELET DECOMPOSITION, INPAINTING AND TEXTURE SYNTHESIS

2009 ◽  
Vol 09 (01) ◽  
pp. 51-65 ◽  
Author(s):  
HUAWEI CHEN ◽  
ICHIRO HAGIWARA ◽  
A. KIET TIEU
Keyword(s):  
High Frequency ◽  
Wavelet Decomposition ◽  
Color Image ◽  
Texture Synthesis ◽  
Low Frequency ◽  
Surface Interpolation ◽  
Digital Reconstruction ◽  
Large Size ◽  
Frequency Level ◽  
Digital Inpainting

Digital inpainting provides a means for reconstruction of damaged portions of an image. Although the inpainting basics are straightforward, most inpainting techniques published in the literature are only suitable for remarkable small portion or smooth color image. In order to avoid such shortcomings, we present a new algorithm for digital reconstruction based on combination of wavelet decomposition, surface-based/PDE-based inpainting and texture synthesis. In this algorithm, wavelet transform firstly decomposes the image into high frequency and low frequency level parts. Subsequently, CSRBF which is generally used for surface interpolation or PDE-based inpainting is employed for low frequency level and texture synthesis is used for high frequency level. It results in that not only slight portion but also the common blotched image can be reconstructed with high quality. Especially, our algorithm makes large-size blotched image possible and becomes more efficient as compared to individual PDE-based and CSRBF approaches.

scholarly journals Smart Image Enhancement Using CLAHE Based on an F-Shift Transformation during Decompression

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1374
Author(s):  
Ruiqin Fan ◽  
Xiaoyun Li ◽  
Sanghyuk Lee ◽  
Tongliang Li ◽  
Hao Lan Zhang
Keyword(s):  
Image Enhancement ◽  
Low Frequency ◽  
Histogram Equalization ◽  
Frequency Component ◽  
High Frequency Component ◽  
Enhancement Effect ◽  
Two Dimensional ◽  
Compression Properties ◽  
Contrast Information ◽  
Image Details

As technologies for image processing, image enhancement can provide more effective information for later data mining and image compression can reduce storage space. In this paper, a smart enhancement scheme during decompression, which combined a novel two-dimensional F-shift (TDFS) transformation and a non-standard two-dimensional wavelet transform (NSTW), is proposed. During the decompression, the first coefficient s00 of the wavelet synopsis was used to adaptively adjust the global gray level of the reconstructed image. Next, the contrast-limited adaptive histogram equalization (CLAHE) was used to achieve the enhancement effect. To avoid a blocking effect, CLAHE was used when the synopsis was decompressed to the second-to-last level. At this time, we only enhanced the low-frequency component and did not change the high-frequency component. Lastly, we used CLAHE again after the image reconstruction. Through experiments, the effectiveness of our scheme was verified. Compared with the existing methods, the compression properties were preserved and the image details and contrast could also be enhanced. The experimental results showed that the image contrast, information entropy, and average gradient were greatly improved compared with the existing methods.

scholarly journals Grass Leaf Identification Using dbN Wavelet and CILBP

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Fan Han ◽  
Xue Qiao ◽  
Yubao Ma ◽  
Weihong Yan ◽  
Xinyu Wang ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Low Frequency ◽  
Texture Features ◽  
Grassland Management ◽  
Frequency Component ◽  
Identification Accuracy ◽  
Human Beings ◽  
The Sustainable Development ◽  
Low Efficiency ◽  
Texture Extraction

Grass is one of the most important resources in the ecosystem for the sustainable development of human beings. However, the studies focusing on grass identification, which were traditionally implemented by experts with low efficiency and precision, cannot meet the requirements of modern grassland management. In this study, we proposed cubic interpolation LBP (CILBP) and dbN wavelets for grass identification based on leaf images. A low-frequency component of leaf images decomposed by dbN wavelets was used as the input of CILBP for more subtle texture extraction. The novelty of the proposed method was that CILBP can better describe the texture features from the low-frequency subimage, as compared with the original bilinear LBP. The effectiveness in identification accuracy of the proposed method for grass leaf was demonstrated by the experimental results.

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