An efficient adaptive fusion scheme for multifocus images in wavelet domain using statistical properties of neighborhood

In this paper we present a novel fusion rule which can efficiently fuse multifocus images in wavelet domain by taking weighted average of pixels. The weights are adaptively decided using the statistical properties of the neighborhood. The main idea is that the eigen value of unbiased estimate of the covariance matrix of an image block depends on the strength of edges in the block and thus makes a good choice for weight to be given to the pixel, giving more weightage to pixel with sharper neighborhood. The performance of the proposed method have been extensively tested on several pairs of multifocus images and also compared quantitatively with various existing methods with the help of well known parameters including Petrovic and Xydeas image fusion metric. Experimental results show that performance evaluation based on entropy, gradient, contrast or deviation, the criteria widely used for fusion analysis, may not be enough. This work demonstrates that in some cases, these evaluation criteria are not consistent with the ground truth. It also demonstrates that Petrovic and Xydeas image fusion metric is a more appropriate criterion, as it is in correlation with ground truth as well as visual quality in all the tested fused images. The proposed novel fusion rule significantly improves contrast information while preserving edge information. The major achievement of the work is that it significantly increases the quality of the fused image, both visually and in terms of quantitative parameters, especially sharpness with minimum fusion artifacts

Comparative Analyses of Multilevel and Geometric Image Fusion Techniques

Image fusion which is a technique to provide the resultant and complete information when two images are combined at a single image. It is widely used application mainly for medical and multifocus imaging. Here in this paper we have proposed combination of multilevel image fusion and geometric based fusion technique. Initially fusion is carried out by multilevel image fusion technique, which includes either wavelet transform or curvelet transform, and at second level fusion is carried out by spatial or laplacian pyramid transform. Further geometric fusion technique will be applied by using the technique of Affine transform. Finally the performance will be evaluated by different quality metrics, which are used to prove the curvelet transform result better performance than wavelet transform in multilevel fusion, and affine transform will produce more resultant than both wavelet and curvelet transform. The proposed system is very unique technique in which this application will be more useful for medical, and satellite imaging. DOI: 10.17762/ijritcc2321-8169.160415

Multifocus image fusion algorithm using iterative segmentation based on edge information and adaptive threshold

This paper presents algorithm for multifocus image fusion in spatial domain based on iterative segmentation and edge information of the source images. The basic idea is to divide the images into smaller blocks, gather edge information for each block and then select the region with greater edge information to construct the resultant 'all-in-focus' fused image. To improve the fusion quality further, an iterative approach is proposed. Each iteration selects the regions in focus with the help of an adaptive threshold while leaving the remaining regions for analysis in the next iteration. A further enhancement in the technique is achieved by making the number of blocks and size of blocks adaptive in each iteration. The pixels which remain unselected till the last iteration are then selected from the source images by comparison of the edge activities in the corresponding segments of the source images. The performance of the method have been extensively tested on several pairs of multifocus images and compared quantitatively with existing methods. Experimental results show that the proposed method improves fusion quality by reducing loss of information by almost 50% and noise by more than 99%

Blending of Images Using Discrete Wavelet Transform

The project presents multi focus image fusion using discrete wavelet transform with local directional pattern and spatial frequency analysis. Multi focus image fusion in wireless visual sensor networks is a process of blending two or more images to get a new one which has a more accurate description of the scene than the individual source images. In this project, the proposed model utilizes the multi scale decomposition done by discrete wavelet transform for fusing the images in its frequency domain. It decomposes an image into two different components like structural and textural information. It doesn’t down sample the image while transforming into frequency domain. So it preserves the edge texture details while reconstructing image from its frequency domain. It is used to reduce the problems like blocking, ringing artifacts occurs because of DCT and DWT. The low frequency sub-band coefficients are fused by selecting coefficient having maximum spatial frequency. It indicates the overall active level of an image. The high frequency sub-band coefficients are fused by selecting coefficients having maximum LDP code value LDP computes the edge response values in all eight directions at each pixel position and generates a code from the relative strength magnitude. Finally, fused two different frequency sub-bands are inverse transformed to reconstruct fused image. The system performance will be evaluated by using the parameters such as Peak signal to noise ratio, correlation and entrop

Survey on wavelet based image fusion techniques

Image fusion is the process of combining multiple images into a single image without distortion or loss of information. The techniques related to image fusion are broadly classified as spatial and transform domain methods. In which, the transform domain based wavelet fusion techniques are widely used in different domains like medical, space and military for the fusion of multimodality or multi-focus images. In this paper, an overview of different wavelet transform based methods and its applications for image fusion are discussed and analysed

Novel Approaches for Regional Multifocus Image Fusion

Image fusion is a research topic about combining information from multiple images into one fused image. Although a large number of methods have been proposed, many challenges remain in obtaining clearer resulting images with higher quality. This chapter addresses the multifocus image fusion problem about extending the depth of field by fusing several images of the same scene with different focuses. Existing research in multifocus image fusion tends to emphasis on the pixel-level image fusion using transform domain methods. The region-level image fusion methods, especially the ones using new coding techniques, are still limited. In this chapter, we provide an overview of regional multi-focus image fusion, and two different orthogonal matching pursuit-based sparse representation methods are adopted for regional multi-focus image fusion. Experiment results show that the regional image fusion using sparse representation can achieve a comparable even better performance for multifocus image fusion problems

Local Energy based Image Fusion in Sharp Frequency Localized Contourlet Transform

Image fusion method based on multiscale transform (MST) is a popular choice in recent research. Sharp frequency localized contourlet transform (SFLCT) that significantly outperform the original contourlet transform is proposed. Commonly, the upsamplers and the downsamplers presented in directional filter banks of SFLCT make the resulting image not shift-invariant and easily cause the pseudo-Gibbs phenomena. In order to suppress the pseudo-Gibbs phenomena, we apply cycle spinning as compensation. Then, the coefficients of shifted images are calculated. We take the following image fusion rules. First, cycle spinning the source images, the shifted images are obtained. Second, selecting the low-frequency coefficients by the local energy method and calculating the high-frequency coefficients by the sum modified Laplacian (SML), and the coefficients fusion follows. Third, applying the inverse SFLCT and the inverse cycle-spinning sequentially, the image is reconstructed. Numerical experiment results show that the proposed method significantly outperform the wavelet transform, the pyramid transform and the curvelet transform both in visual quality and in quantitative analysis