A practical pan-sharpening method with wavelet transform and sparse representation

2013 ◽  
Author(s):  
Yu Liu ◽  
Zengfu Wang
Keyword(s):  
Wavelet Transform ◽  
Sparse Representation

Infrared and visible image fusion using dual-tree complex wavelet transform and convolutional sparse representation

2020 ◽  
Vol 39 (3) ◽  
pp. 4617-4629
Author(s):  
Chengrui Gao ◽  
Feiqiang Liu ◽  
Hua Yan
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Sparse Representation ◽  
High Frequency ◽  
Low Frequency ◽  
Infrared Images ◽  
Complex Wavelet Transform ◽  
Visible Image ◽  
Visible Images ◽  
Complex Wavelet

Infrared and visible image fusion refers to the technology that merges the visual details of visible images and thermal feature information of infrared images; it has been extensively adopted in numerous image processing fields. In this study, a dual-tree complex wavelet transform (DTCWT) and convolutional sparse representation (CSR)-based image fusion method was proposed. In the proposed method, the infrared images and visible images were first decomposed by dual-tree complex wavelet transform to characterize their high-frequency bands and low-frequency band. Subsequently, the high-frequency bands were enhanced by guided filtering (GF), while the low-frequency band was merged through convolutional sparse representation and choose-max strategy. Lastly, the fused images were reconstructed by inverse DTCWT. In the experiment, the objective and subjective comparisons with other typical methods proved the advantage of the proposed method. To be specific, the results achieved using the proposed method were more consistent with the human vision system and contained more texture detail information.

scholarly journals Anomaly targets detection of hyperspectral imagery based on wavelet transform and sparse representation

2018 ◽  
Vol 232 ◽  
pp. 02054
Author(s):  
Cheng Baozhi
Keyword(s):  
Wavelet Transform ◽  
Sparse Representation ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Image Data ◽  
Detection Algorithm ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Low Efficiency

The research of anomaly target detection algorithm in hyperspectral imagery is a hot issue, which has important research value. In order to overcome low efficiency of current anomaly target detection in hyperspectral image, an anomaly detection algorithm for hyperspectral images based on wavelet transform and sparse representation was proposed. Firstly, two-dimensional discrete wavelet transform is used to denoise the hyperspectral image, and the new hyperspectral image data are obtained. Then, the results of anomaly target detection are obtained by using sparse representation theory. The real AVIRIS hyperspectral imagery data sets are used in the experiments. The results show that the detection accuracy and false alarm rate of the propoesd algorithm are better than RX and KRX algorithm.

Sparse representation of image and video using easy path wavelet transform

2014 ◽  
Vol 103 ◽  
pp. 331-347 ◽  
Author(s):  
Syed Akbar Raza Naqvi ◽  
Imran Touqir ◽  
Adil Masood Siddiqui
Keyword(s):  
Wavelet Transform ◽  
Sparse Representation

scholarly journals Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain

2021 ◽  
Vol 3 (3) ◽  
pp. 218-233
Author(s):  
R. Dhaya
Keyword(s):  
Wavelet Transform ◽  
Sparse Representation ◽  
Structural Characteristics ◽  
Threshold Value ◽  
Transform Domain ◽  
Wireless Multimedia ◽  
Complex Wavelet Transform ◽  
Denoising Method ◽  
Suggested Technique ◽  
Complex Wavelet

In recent years, there has been an increasing research interest in image de-noising due to an emphasis on sparse representation. When sparse representation theory is compared to transform domain-based image de-noising, the former indicates that the images have more information. It contains structural characteristics that are quite similar to the structure of dictionary-based atoms. This structure and the dictionary-based method is highly unsuccessful. However, image representation assumes that the noise lack such a feature. The dual-tree complex wavelet transform incorporates an increase in transform data density to reduce the effects of sparse data. This technique has been developed to decrease the image noise by selecting the best-predicted threshold value derived from wavelet coefficients. For our experiment, Discrete Cosine Transform (DCT) and Complex Wavelet Transform (CWT) are used to examine how the suggested technique compares the conventional DCT and CWT on sets of realistic images. As for image quality measures, DT-CWT has leveraged superior results. In terms of processing time, DT-CWT gave better results with a wider PSNR range. Further, the proposed model is tested with a standard digital image named Lena and multimedia sensor images for the denoising algorithm. The suggested denoising technique has delivered minimal effect on the MSE value.

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