Image enhancement using fusions by Wavelet Transform, Laplacian Pyramid and combination of both.

This paper represents idea of combining multiple image modalities to provide a single, enhanced image is well established different fusion methods have been proposed in literature. This paper is based on image fusion using wavelet transform, laplacian pyramid and combination of laplacian pyramid and wavelet transform method. Images of same size are used for experimentation. Images used for the experimentation are standard images and averaging filter is used of equal weights in original images to burl. Performance of image fusion technique is measured by mean square error, normalized absolute error and peak signal to noise ratio. proposed method is compared with wavelet transform method and laplacian pyramid method, from the performance analysis it has been observed that MSE is decreased in case of all three the methods where as PSNR Increased, NAE decreased in case of laplacian pyramid and Combination of laplacian pyramid and wavelet transform where as constant for wavelet transform method

Image fusion using Wavelet Transform: A Review

An Image fusion is the development of amalgamating two or more image of common characteristic to form a single image which acquires all the essential features of original image Nowadays lots of work is going to be done on the field of image fusion and also used in various application such as medical imaging and multi spectra sensor image fusing etc For fusing the image various techniques has been proposed by different author such as wavelet transform IHS and PCA based methods etc In this paper literature of the image fusion with wavelet transform is discussed with its merits and demerit

Infrared and Visible Image Fusion Based on Oversampled Graph Filter Banks

The infrared image (RI) and visible image (VI) fusion method merges complementary information from the infrared and visible imaging sensors to provide an effective way for understanding the scene. The graph filter bank-based graph wavelet transform possesses the advantages of the classic wavelet filter bank and graph representation of a signal. Therefore, we propose an RI and VI fusion method based on oversampled graph filter banks. Specifically, we consider the source images as signals on the regular graph and decompose them into the multiscale representations with M-channel oversampled graph filter banks. Then, the fusion rule for the low-frequency subband is constructed using the modified local coefficient of variation and the bilateral filter. The fusion maps of detail subbands are formed using the standard deviation-based local properties. Finally, the fusion image is obtained by applying the inverse transform on the fusion subband coefficients. The experimental results on benchmark images show the potential of the proposed method in the image fusion applications

A New Technology of Remote Sensing Image Fusion

Wavelet packet transform stands out in the field of image fusion for its good frequency characteristics, and pulse coupled neural network (PCNN) has a unique advantage in image processing. To resolve the problem of multi-spectral remote sensing image fusion, in this paper, we put forward an algorithm combined the wavelet packet and PCNN based on HIS transform.The algorithm will be carried out as follows. Firstly, the TM images will be converted into HIS space, and then the luminance component and the high-resolution image will be broken into multi-scale by wavelet packet. Secondly, according to the frequency domain characteristics of the wavelet packet decomposition, we respectively use a method of weighted average in the low-frequency domain and a method of PCNN in the high frequency domain to select reconstruction coefficient.We can get a fused luminance component by taking inverse wavelet packet transform to be reconstructed. Finally, we can obtain the fusion image by taking inverse HIS transform. The experimental results show that the algorithm can be not only to retain the spectral information, but also greatly improve the spatial resolution of multispectral images, has a good fusion effec

Research on Target Detection Algorithm of Radar and Visible Image Fusion Based on Wavelet Transform

The target detection rate of unmanned surface vehicle is low because of waves, fog, background clutter and other environmental factors on the interference. Therefore, the paper studies the target detection algorithm of radar and visible image fusion based on wavelet transform. The visible image is preprocessed to ensure the detection effect. The multi-scale fractal model is used to extract the target features, and the difference between the fractal features of the target and the background is used to detect the target. The radar image is denoised by a combination of median filtering and wavelet transform. The processed visible light and radar image are fused with wavelet transform strategy. The coefficients of the low frequency sub-band are processed by the average fusion strategy. The coefficients of the high frequency sub-band are processed using a strategy with a higher absolute value. The standard deviation, the spatial frequency and the contrast resolution of the image fusion result are compared. The simulation results show that the processed image is better than the unprocessed image after the fusion