scholarly journals Image Reconstruction from Multiscale Singular Points Based on the Dual-Tree Complex Wavelet Transform

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Sihang Liu ◽  
Benoit Tremblais ◽  
Phillippe Carre ◽  
Nanrun Zhou ◽  
Jianhua Wu
Keyword(s):  
Wavelet Transform ◽  
Image Reconstruction ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
Structural Similarity ◽  
Singular Points ◽  
Main Concern ◽  
Complex Wavelet Transform ◽  
Orthogonal Projections ◽  
Complex Wavelet

The representation of an image with several multiscale singular points has been the main concern in image processing. Based on the dual-tree complex wavelet transform (DT-CWT), a new image reconstruction (IR) algorithm from multiscale singular points is proposed. First, the image was transformed by DT-CWT, which provided multiresolution wavelet analysis. Then, accurate multiscale singular points for IR were detected in the DT-CWT domain due to the shift invariance and directional selectivity properties of DT-CWT. Finally, the images were reconstructed from the phases and magnitudes of the multiscale singular points by alternating orthogonal projections between the CT-DWT space and its affine space. Theoretical analysis and experimental results show that the proposed IR algorithm is feasible, efficient, and offers a certain degree of denoising. Furthermore, the proposed IR algorithm outperforms other classical IR algorithms in terms of performance metrics such as peak signal-to-noise ratio, mean squared error, and structural similarity.

scholarly journals Dual tree complex wavelet transform based image denoising for Kalpana satellite images

2018 ◽  
Vol 7 (3.29) ◽  
pp. 269
Author(s):  
Naga Lingamaiah Kurva ◽  
S Varadarajan
Keyword(s):  
Wavelet Transform ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Pictorial Representation ◽  
Complex Wavelet Transform ◽  
Raw Data ◽  
Complex Wavelet ◽  
Kalpana Satellite

This paper presents a new algorithm to reduce the noise from Kalpana Satellite Images using Dual Tree Complex Wavelet Transform technique. Satellite Images are not simple photographs; they are pictorial representation of measured data. Interpretation of noisy raw data leads to wrong estimation of geophysical parameters such as precipitation, cloud information etc., hence there is a need to improve the raw data by reducing the noise for better analysis. The satellite images are normally affected by various noises. This paper mainly concentrates on reducing the Gaussian noise, Poisson noise and Salt & Pepper noise. Finally the performance of the DTCWT wavelet measures in terms of Peak Signal to Noise Ratio and Structural Similarity Index for both noisy & denoised Kalpana images.   

Quantum and dual-tree complex wavelet transform-based image watermarking

2020 ◽  
Vol 34 (04) ◽  
pp. 2050009 ◽  
Author(s):  
Deepika Ghai ◽  
Hemant Kumar Gianey ◽  
Arpit Jain ◽  
Raminder Singh Uppal
Keyword(s):  
Wavelet Transform ◽  
Performance Metrics ◽  
Image Watermarking ◽  
Multimedia Data ◽  
Watermark Image ◽  
Complex Wavelet Transform ◽  
Public Networks ◽  
Transform Coefficients ◽  
Complex Wavelet ◽  
Value Decomposition

Nowadays, multimedia applications are extensively utilized and communicated over Internet. Due to the use of public networks for communication, the multimedia data are prone to various security attacks. In the past few decades, image watermarking has been extensively utilized to handle this issue. Its main objective is to embed a watermark into a host multimedia data without affecting its presentation. However, the existing methods are not so effective against multiplicative attacks. Therefore, in this paper, a novel quantum-based image watermarking technique is proposed. It initially computes the dual-tree complex wavelet transform coefficients of an input cover image. The watermark image is then scrambled using Arnold transform. Thereafter, in the lower coefficient input the watermark image is embedded using quantum-based singular value decomposition (SVD). Finally, the covered image is obtained by applying the inverse dual-tree complex wavelet transform on the obtained coefficients. Comparative analyses are carried out by considering the proposed and the existing watermarking techniques. It has been found that the proposed technique outperforms existing watermarking techniques in terms of various performance metrics.

scholarly journals Lifting dual tree complex wavelets transform

2021 ◽  
Vol 11 (5) ◽  
pp. 4008
Author(s):  
Hilal Naimi ◽  
Amelbahahouda Adamou-Mitiche ◽  
Lahcène Mitiche
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Computation Time ◽  
Structural Similarity ◽  
Complex Wavelet Transform ◽  
Complex Wavelets ◽  
Wavelets Transform ◽  
Complex Wavelet ◽  
Segmentation Image

We describe the lifting dual tree complex wavelet transform (LDTCWT), a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). We describe a way to estimate the accuracy of this approximation and style appropriate filters to attain this. These benefits are often exploited among applications like denoising, segmentation, image fusion and compression. The results of applications shrinkage denoising demonstrate objective and subjective enhancements over the dual tree complex wavelet transform (DTCWT). The results of the shrinkage denoising example application indicate empirical and subjective enhancements over the DTCWT. The new transform with the DTCWT provide a trade-off between denoising computational competence of performance, and memory necessities. We tend to use the PSNR (peak signal to noise ratio) alongside the structural similarity index measure (SSIM) and the SSIM map to estimate denoised image quality.

scholarly journals Dual Stage Bayesian Network with Dual-Tree Complex Wavelet Transformation for Image Denoising

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Venkata Lavanya P. ◽  
Venkata Narasimhulu C. ◽  
Satya Prasad K.
Keyword(s):  
Mean Squared Error ◽  
Structural Similarity ◽  
Absolute Error ◽  
Noise Spectrum ◽  
Percentage Error ◽  
Wavelet Coefficients ◽  
Complex Wavelet Transform ◽  
Proposed Model ◽  
Complex Wavelet ◽  
Image Type

Image de-noising always plays a vital role in various engineering bids. Moreover, in image processing technology, image de-noising statistics is persisted as a substantial dispute. Over the past decades, certain de-noising methods have been exposed incredible accomplishments. This paper intends to develop a de-noising algorithm for multimodal and heterogeneous images, while the conventional de-noising algorithms handle a specific image type. The filtered information is reversed to spatial domain to recover the de-noised image. Dual tree Complex Wavelet Transform (DT-CWT) is exploited for image transformation for which the wavelet coefficients are estimated using Bayesian Regularization (BR). To ensure the de-noising performance for heterogeneous images, the statistical and wavelet features are extracted. Subsequently, the image characteristics are combined with noise spectrum to develop BR model, which estimates the wavelet coefficients for effective de-noising. Hence, the proposed de-noising algorithm exploits two stages of BR. The first stage predicts the image type, whereas the second stage estimates appropriate wavelet coefficients to DT-CWT for de-noising. The performance of the proposed model is analysed in terms of Peak Signal to Noise Ratio (PSNR), Second derivative Measure of Enhancement (SDME), Structural Similarity (SSIM), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson Coefficient (PC), and Symmetric Mean Absolute Percentage Error (SMAPE) respectively. The proposed model is compared to the conventional models, and the significance of the developed model is clearly described.

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