Image Fusion In Wavelet Domain Research Articles

Compressive sensing image-fusion algorithm in wireless sensor networks based on blended basis functions

Compressive sensing (CS) has given us a new idea at data acquisition and signal processing. It has proposed some novel solutions in many practical applications. Focusing on the pixel-level multi-source image-fusion problem in wireless sensor networks, the paper proposes an algorithm of CS image fusion based on multi-resolution analysis. We present the method to decompose the images by nonsubsampled contourlet transform (NSCT) basis function and wavelet basis function successively and fuse the images in compressive domain. It means that the images can be sparsely represented by more than one basis function. We named this process as blended basis functions representation. Since the NSCT and wavelet basis functions have complementary advantages in multi-resolution image analysis, and the signals are sparser after being decomposed by two kinds of basis functions, the proposed algorithm has perceived advantages in comparison with CS image fusion in wavelet domain which is widely reported by literatures. The simulations show that our method provides promising results.

Wavelet-domain satellite image fusion based on a generalized fusion equation

A generalized wavelet domain image fusion method which imposes weights on each of the wavelet coefficients for improving the conventional wavelet domain approach is pre- sented. The weights are controlled in the least-squares sense for enhancing the details while suppressing excessive high frequency components. In experiments with IKONOS and QuickBird satellite data, we demonstrated that the proposed method shows a comparable or better performance than conventional methods in terms of various objective quality metrics. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original pub- lication, including its DOI. (DOI: 10.1117/1.JRS.8.080599)

Adaptive multi-focus image fusion using a wavelet-based statistical sharpness measure

Multi-focus image fusion aims to combine a set of images that are captured from the same scene but with different focuses for producing another sharper image. The critical issue in the design of multi-focus image fusion algorithms is to evaluate the local content information of the input images. Motivated by the observation that the marginal distribution of the wavelet coefficients is different for images with different focus levels, a new statistical sharpness measure is proposed in this paper by exploiting the spreading of the wavelet coefficients distribution to measure the degree of the image's blur. Furthermore, the wavelet coefficients distribution is evaluated using a locally adaptive Laplacian mixture model. The proposed sharpness measure is then exploited to perform adaptive image fusion in wavelet domain. Extensive experiments are conducted using three sets of test images under three objective metrics to demonstrate the superior performance of the proposed approach.