In the fluorescence detection system, noise will affect the sensitivity of the system. The method using Stationary Wavelet Transform to de-noise fluorescence signals based on adaptive wavelet entropy is studied. The de-noising effects of dbN families are compared, and then the db7 wavelet is chosen as the optimal wavelet. The noised fluorescence signal is decomposed at 5 levels via Discrete Wav...

In this paper fusion of visual and thermal images in wavelet transformed domain has been presented. Here, Daubechies wavelet transform, called as D2, coefficients from visual and corresponding coefficients computed in the same manner from thermal images are combined to get fused coefficients. After decomposition up to fifth level (Level 5) fusion of coefficients is done. Inverse Daubechies wave...

Today, image fusion as one kind of information integrated technology has played an important role in many fields. This paper presents a novel wavelet based technique for image fusion, which is developed by taking into account the physical meaning of the wavelet coefficients. The images to be fused are firstly decomposed into high frequency and low frequency bands by wavelet transform. Then, by ...

In the last few years, it has become apparent that traditional wavelet-based image processing algorithms and models have significant shortcomings in their treatment of edge contours. The standard modeling paradigm exploits the fact that wavelet coefficients representing smooth regions in images tend to have small magnitude, and that the multiscale nature of the wavelet transform implies that th...

Wavelet domain algorithms have risen to the forefront of image processing. The power of these algorithms is derived from the fact that the wavelet transform restructures images in a way that makes statistical modeling simpler. Since edge singularities account for the most important information in images, understanding how edges lbehave in the wavelet domain is the key to modeling. In the past, ...

We describe a new filtering approach in the wavelet domain for image denoising and compression, based on the projections of details subbands coefficients (resultants of the splitting procedure, typical in wavelet domain) onto the approximation subband coefficients (much less noisy). The new algorithm is called Projection Onto Approximation Coefficients (POAC). As a result of this approach, only...

Wavelet methods have demonstrated considerable success in function estimation through term-by-term thresholding of the empirical wavelet coefficients. However, it has been shown that grouping the empirical wavelet coefficients into blocks and making simultaneous threshold decisions about all the coefficients in each block has a number of advantages over term-by-term wavelet thresholding, includ...

Image denoising using wavelet transform has been successful as wavelet transform generates a large number of small coefficients and a small number of large coefficients. Basic denoising algorithm that using the wavelet transform consists of three steps – first computing the wavelet transform of the noisy image, thresholding is performed on the detail coefficients in order to remove noise and fi...

Wavelet based image compression has proved its superiority over DCT based methods. However, wavelet based techniques did not have so much success in video coding because motion compensation usually produces residuals that are not suitable for wavelet compression. This paper discusses a motion compensation technique which should fit naturally into wavelet based compression schemes. The motion co...

This paper describes an approach to image modelling using interscale phase relationships of wavelet coefficients for use in image estimation applications. The method is based on the Dual Tree Complex Wavelet Transform (DT-CWT) but a phase rotation is applied to the coefficients to create complex ‘derotated’ coefficients. These derotated coefficients are shown to have increased correlation compa...