scholarly journals Restoration of Noisy Blurred Images Using MFPIA and Discrete Wavelet Transform

2013 ◽  
Vol 9 (1) ◽  
pp. 1-15
Author(s):  
Dunia Tahir
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Gaussian Noise ◽  
Motion Blur ◽  
Discrete Wavelet ◽  
Wavelet Thresholding ◽  
Fixed Phase ◽  
Approximation Coefficients ◽  
Blurred Images ◽  
Deblurring And Denoising

In this paper, image deblurring and denoising are presented. The used images were blurred either with Gaussian or motion blur and corrupted either by Gaussian noise or by salt & pepper noise. In our algorithm, the modified fixed-phase iterative algorithm (MFPIA) is used to reduce the blur. Then a discrete wavelet transform is used to divide the image into two parts. The first part represents the approximation coefficients. While the second part represents the detail coefficients, that a noise is removed by using the BayesShrink wavelet thresholding method.

scholarly journals Discrete Wavelet Transform Based Image Fusion Using Unsharp Masking

2019 ◽  
Vol 64 (2) ◽  
pp. 211-220
Author(s):  
Sumanth Kumar Panguluri ◽  
Laavanya Mohan
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Fusion Rule ◽  
Discrete Wavelet ◽  
Visible Image ◽  
Unsharp Masking ◽  
Fused Image ◽  
Approximation Coefficients ◽  
Performance Results

Nowadays the result of infrared and visible image fusion has been utilized in significant applications like military, surveillance, remote sensing and medical imaging applications. Discrete wavelet transform based image fusion using unsharp masking is presented. DWT is used for decomposing input images (infrared, visible). Approximation and detailed coefficients are generated. For improving contrast unsharp masking has been applied on approximation coefficients. Then for merging approximation coefficients produced after unsharp masking average fusion rule is used. The rule that is used for merging detailed coefficients is max fusion rule. Finally, IDWT is used for generating a fused image. The result produced using the proposed fusion method is providing good contrast and also giving better performance results in reference to mean, entropy and standard deviation when compared with existing techniques.

scholarly journals Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis

2019 ◽  
Vol 11 (11) ◽  
pp. 1331 ◽  
Author(s):  
Fenling Li ◽  
Li Wang ◽  
Jing Liu ◽  
Yuna Wang ◽  
Qingrui Chang
Keyword(s):  
Wavelet Transform ◽  
Winter Wheat ◽  
Discrete Wavelet Transform ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Growth Stages ◽  
Discrete Wavelet ◽  
Canopy Reflectance ◽  
Important Indicator ◽  
Approximation Coefficients

Leaf nitrogen concentration (LNC) is an important indicator for accurate diagnosis and quantitative evaluation of plant growth status. The objective was to apply a discrete wavelet transform (DWT) analysis in winter wheat for the estimation of LNC based on visible and near-infrared (400–1350 nm) canopy reflectance spectra. In this paper, in situ LNC data and ground-based hyperspectral canopy reflectance was measured over three years at different sites during the tillering, jointing, booting and filling stages of winter wheat. The DWT analysis was conducted on canopy original spectrum, log-transformed spectrum, first derivative spectrum and continuum removal spectrum, respectively, to obtain approximation coefficients, detail coefficients and energy values to characterize canopy spectra. The quantitative relationships between LNC and characteristic parameters were investigated and compared with models established by sensitive band reflectance and typical spectral indices. The results showed combining log-transformed spectrum and a sym8 wavelet function with partial least squares regression (PLS) based on the approximation coefficients at decomposition level 4 most accurately predicted LNC. This approach could explain 11% more variability in LNC than the best spectral index mSR705 alone, and was more stable in estimating LNC than models based on random forest regression (RF). The results indicated that narrowband reflectance spectroscopy (450–1350 nm) combined with DWT analysis and PLS regression was a promising method for rapid and nondestructive estimation of LNC for winter wheat across a range in growth stages.

scholarly journals SlantletTransformbased VideoDenoising

2011 ◽  
Vol 8 (2) ◽  
pp. 595-601
Author(s):  
Baghdad Science Journal
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Gaussian Noise ◽  
Noise Removal ◽  
Experimental Results ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Frame Sequence ◽  
Objective Quality ◽  
Original Frame

A technique for noise removal is proposed based on slantlet transform. The proposed algorithm tends to reduce the computational time by reducing the total number of frames through dividing the video film into sub films, finding master frames, applying the slantlet transform which is orthogonal discrete wavelet transform with two zero moments and with improved time localization. Thresholding technique is applied to the details coefficients of the slantlet transform .The denoised frame is repeated to retain the original frame sequence. The proposed method was applied by using MATLAB R2010a with video contaminated by white Gaussian noise .The experimental results show that the proposed method provides better subjective and objective quality, and obtain up to 5-6 dB PSNR improvement from the frames contaminated by noise.

TWO-DIMENSIONAL STATIONARY DYADIC WAVELET TRANSFORM, DECIMATED DYADIC DISCRETE WAVELET TRANSFORM AND THE FACE RECOGNITION APPLICATION

2011 ◽  
Vol 09 (03) ◽  
pp. 397-416 ◽  
Author(s):  
YANKUI SUN ◽  
YONG CHEN ◽  
HAO FENG
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Wavelet Filters ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform ◽  
Approximation Coefficients

Currently, two-dimensional dyadic wavelet transform (2D-DWT) is habitually considered as the one presented by Mallat, which is defined by an approximation component, two detail components in horizontal and vertical directions. This paper is to introduce a new type of two-dimensional dyadic wavelet transform and its application so that dyadic wavelet can be studied and used widely furthermore. (1) Two-dimensional stationary dyadic wavelet transform (2D-SDWT) is proposed, it is defined by approximation coefficients, detail coefficients in horizontal, vertical and diagonal directions, which is essentially the extension of two-dimensional stationary wavelet transform for orthogonal/biorthogonal wavelet filters. (2) ε-decimated dyadic discrete wavelet transform (DDWT) is introduced and its relation with 2D-SDWT is given, where ε is a sequence of 0's and 1's. (3) Mallat decomposition algorithm based on dyadic wavelet is introduced as a special case of ε-decimated DDWT, and so a face recognition algorithm based on dyadic wavelet is proposed, and experimental results are given to show its effectiveness.

Improving Space Localization Properties of the Discrete Wavelet Transform

Informatica ◽  
2013 ◽  
Vol 24 (4) ◽  
pp. 657-675
Author(s):  
Jonas Valantinas ◽  
Deividas Kančelkis ◽  
Rokas Valantinas ◽  
Gintarė Viščiūtė
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Space Localization
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