Multi-focus Image Fusion Algorithm Research Articles

Multifocus Image Fusion Based on Multiresolution Transform and Particle Swarm Optimization

In order to get an image with every object in focus, an image fusion process is required to fuse the images under different focal settings. In this paper, a novel multifocus image fusion algorithm based on multiresolution transform and particle swarm optimization (PSO) is proposed. Firstly the source images are decomposed into lowpass subbands coefficients and highpass subbands coefficients by the nonsubsampled contourlet transform (NSCT). Then, different fusion rules are applied for low and high frequency NSCT coefficients. Finally the fused image is reconstructed by the inverse NSCT transform. The experiment results demonstrate that the proposed method is effective and can provide better performance than the method based on the wavelet transform and the nonsubsampled contourlet transform.

Multi-focus image fusion algorithm based on compound PCNN in Surfacelet domain

In order to effectively retain details and suppress noise, a multi-focus image fusion method based on Surfacelet transform and compound PCNN is proposed. Surfacelet transform is a powerful multi-resolution analysis tool which is able to decompose the original image into a number of different frequency band sub-images, compound PCNN model is a combined model of PCNN and dual-channel PCNN which is to select the fusion coefficients from the decomposed coefficients, the Local sum-modified-Laplacian (LSML) is selected as external stimulus of compound PCNN, fusion coefficients are decided by compound PCNN. The experimental results show that the new method has a good performance, fusion image has more texture details and it is more similar to the original images, the objective evaluation indexes show that this method is superior to the traditional image fusion methods.

Multifocus Image Fusion Using Laplacian Pyramid and Gabor Filters

In order to get an image with every object in focus, an image fusion process is required to fuse the images under different focal settings. In this paper, a new multifocus image fusion algorithm is proposed. The algorithm is based on Laplacian pyramid and Gabor filters. The source images are decomposed by Laplacian pyramid, then the directional edges feature and detail information can be obtained by Gabor filters. Different fusion rules are applied to the low frequency and high frequency coefficients. The experimental results show that the algorithm is simple and effective.

Multi‐focus image fusion based on non‐subsampled shearlet transform

In this study, a new multi-focus image fusion algorithm based on the non-subsampled shearlet transform (NSST) is presented. First, an initial fused image is acquired by using a conventional multi-resolution image fusion method. The pixels of those source multi-focus images, which have smaller square error with the corresponding pixels of the initial fused image, are considered in the focused regions. Based on this principle, the focused regions are determined, and the morphological opening and closing are employed for post-processing. Then the focused regions and the focused border regions in each source image are identified and used to guide the fusion process in NSST domain. Finally, the fused image is obtained using the inverse NSST. Experimental results show that this proposed method can not only extract more important detailed information from source images, but also avoid the introduction of artificial information effectively. It significantly outperforms the discrete wavelet transform (DWT)-based fusion method, the non-subsampled contourlet-transformbased fusion method and the NSST-based fusion method (see Miao et al. 2011) in terms of both visual quality and objective evaluation.

Multifocus image fusion algorithm based on Brenner function and new contourlet transform

In order to eliminate the aliasing phenomena of the spectrum in each direction subband of the fusion algorithm in contourlet domain and to improve the accuracy of extracting effective coefficients,a multifocus image fusion method was proposed based on the Brenner function and the New Contourlet Transform with Sharp Frequency Localization(NCT-SFL).Firstly,the new contourlet transform was used to decompose the multifocus images which to be fused.And then the traditional arithmetic mean fusion rule was used to fuse the low-frequency coefficients and the maximum local energy based on the Brenner function to fuse the high-frequency coefficients.Finally,the inverse new contourlet transform was used to obtain fused images.Using the proposed fusion method,the experimental results demonstrate that the algorithm can effectively extract the contour information of the images which to be fused,and under the premise of obtaining a better performance in terms of subjective vision,objective evaluation criterions of mutual information and transferred edge information have improved by 99.34% and 77.95% respectively.In addition,the more levels of the new contourlet decomposition,the more obvious advantages of the algorithm will show.

A Novel Multifocus Image Fusion Algorithm Based on Nonsubsampled Contourlet Transform

A novel multifocus image fusion algorithm based on NSCT is proposed in this paper. In order to not only attain the image focusing properties and more visual information in the fused image, but also sensitive to the human visual perception, a local multidirection variance (LEOV) fusion rule is proposed for lowpass subband coefficient. In order to introduce more visual saliency, a modified local contrast is defined. In addition, according to the feature of distribution of highpass subband coefficients, a direction vector is proposed to constrain the modified local contrast and construct the new fusion rule for highpass subband coefficients selection The NSCT is a flexible multiscale, multidirection, and shift-invariant tool for image decomposition, which can be implemented via the atrous algorithm. The proposed fusion algorithm based on NSCT not only can prevent artifacts and erroneous from introducing into the fused image, but also can eliminate block effect and frequency aliasing phenomenon. Experimental results show that the proposed method achieved better fusion results than wavelet-based and CT-based fusion method in contrast and clarity.

A multi-focus image fusion algorithm based on an improved dual-channel PCNN in NSCT domain

This paper presents a multi-focus image fusion algorithm based on dual-channel PCNN in NSCT domain. The fusion algorithm based on multi-scale transform is likely to produce the pseudo-Gibbs effects and it is not effective to fuse the dim or partial bright images. To solve these problems, this algorithm will get a number of different frequency sub-image of the two images by using the NSCT transform, the selection principles of different subband coefficients obtained by the NSCT decomposition are discussed in detail, and the images are fused based on the improved dual-channel PCNN in order to determine the band-pass sub-band coefficient, at last fused image is obtained by using the inverse NSCT transform. Fusion rules based on dual-channel PCNN are used to solve the complexity of the PCNN parameter settings and long computing time problems. The experimental results show that the algorithm has overcome the defects of the traditional multi-focus image fusion algorithm and improved the fusion effect.

Multi-band Vector Wavelet Transformation based Multi-Focus Image Fusion Algorithm

Multi-focus image fusion is one of the important embranchments of image fusion. It has been widely used in target identification, remote sensing image processing and so on. In this paper, a new multi-focus image fusion method based on multi-band vector wavelet is presented. Furthermore, some post processing is done in this paper, in which anisotropic diffusion arithmetic based on partial differential equations is used. As for the fusion image, the blocking effects which usually existed in the results are eliminated by using wavelet based image fusion method. To test and evaluate the proposed method, it is applied to a case study to demonstrate its performance in image fusion. Comparisons of experimental results by using several methods demonstrated the effectiveness of our proposed methods.

Noise suppressed, multifocus image fusion for enhanced intraoperative navigation.

Current intraoperative imaging systems are typically not able to provide 'sharp' images over entire large areas or entire organs. Distinct structures such as tissue margins or groups of malignant cells are therefore often difficult to detect, especially under low signal-to-noise-ratio conditions. In this report, we introduce a noise suppressed multifocus image fusion algorithm, that provides detailed reconstructions even when images are acquired under sub-optimal conditions, such is the case for real time fluorescence intraoperative surgery. The algorithm makes use of the Anscombe transform combined with a multi-level stationary wavelet transform with individual threshold-based shrinkage. While the imaging system is integrated with a respiratory monitor triggering system, it can be easily adapted to any commercial imaging system. The developed algorithm is made available as a plugin for Osirix.

A New Multi-Focus Image Fusion Algorithm Based on Contrast Ratio and Discrete Wavelet Frame Transform

This paper proposes a multi-focus image fusion algorithm based on contrast ratio and discrete wavelet frame transform. Firstly, this algorithm uses wavelet transform to perform the wavelet decomposition of the source image, and then obtains the high-frequency sub-band coefficients after the discrete wavelet frame transform to reflect the details of the image, finally, gets the fusion image obtained by wavelet reconstruction. Using evaluation indicators of information entropy, standard deviation, average gradient and spatial frequency, it objectively evaluates the fusion quality of this algorithm. The experimental results show that the quality and effect of the fusion image derived from the algorithm are significantly improved.

Multi-focus image fusion algorithm based on region energy framework

In the multi-focus image fusion algorithm based on wavelet transform,the quality of fused images is usually reduced due to the discrepancy between the adjacent pixels.According to the transform domain of the fusion rule,the eigenvalues of region energy were extracted by using the coefficients of the wavelet transform,and the global match degree was regarded as a way to determine the fusion rules.A new fusion algorithm based on region energy framework was proposed.To improve the quality of fused images,the consistency of the source of each wavelet component was detected.Finally,the algorithm was simulated in experiments.The results verify that this algorithm has better fusion effects than those based on weighted average,selecting the largest gray value and region energy.

Multi-focus image fusion based on nonsubsampled contourlet transform and focused regions detection

A novel multi-focus image fusion algorithm is proposed in nonsubsampled contourlet transform (NSCT) domain. It uses the technique of focused regions detection and some new fusion rules to guide coefficients combination. At first, the initial fused image is acquired with one of conventional multiresolution image fusion method; meanwhile the source multi-focus images are decomposed by using NSCT. The pixels of the original images, which are more similar to the corresponding pixels of initial fused image, are considered from the sharply focused regions. Based on this, the initial focused regions can be determined and the morphological opening and closing are employed for postprocessing. Then the focused regions in each image are identified and used to guide the different subband coefficients combination. Moreover, in order to avoid the erroneous results introduced at the boundary of the focused regions, the coefficients located at the focused border regions are fused by the choice of corresponding subband coefficients according to some new fusion rules. Finally, the fused image is reconstructed by performing the inverse NSCT. The experimental results show that the proposed fusion approach is effective and can provide better performance in fusing multi-focus images than some current methods.

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.

Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure

Finite depth-of-field poses a problem in light optical imaging systems since the objects present outside the range of depth-of-field appear blurry in the recorded image. Effective depth-of-field of a sensor can be enhanced considerably without compromising the quality of the image by combining multi-focus images of a scene. This paper presents a block-based algorithm for multi-focus image fusion. In general, finding a suitable block-size is a problem in block-based methods. A large block is more likely to contain portions from both focused and defocused regions. This may lead to selection of considerable amount of defocused regions. On the other hand, small blocks do not vary much in relative contrast and hence difficult to choose from. Moreover, small blocks are more affected by mis-registration problems. In this work, we present a block-based algorithm which do not use a fixed block-size and rather makes use of a quad-tree structure to obtain an optimal subdivision of blocks. Though the algorithm starts with blocks, it ultimately identifies sharply focused regions in input images. The algorithm is simple, computationally efficient and gives good results. A new focus-measure called energy of morphologic gradients is introduced and is used in the algorithm. It is comparable with other focus measures viz.energy of gradients, variance, Tenengrad, energy of Laplacian and sum modified Laplacian. The algorithm is robust since it works with any of the above focus measures. It is also robust against pixel mis-registration. Performance of the algorithm has been evaluated by using two different quantitative measures.

A Multi-focus Image Fusion Method Based on Laplacian Pyramid

This paper presented a simple and efficient algorithm for multi-focus image fusion, which used a multi-resolution signal decomposition scheme called Laplacian pyramid method. The principle of Laplacian pyramid transform is introduced, and based on it the fusion strategy is described in detail. The method mainly composed of three steps. Firstly, the Laplacian pyramids of each source image are deconstructed separately, and then each level of new Laplacian pyramid is fused by adopting different fusion rules. To the top level, it adopts the maximum region information rule; and to the rest levels, it adopts the maximum region energy rule. Finally, the fused image is obtained by inverse Laplacian pyramid transform. Two sets of images are applied to verify the fusion approach proposed and compared it with other fusion approaches. By analyzing the experimental results, it showed that this method has good performance, and the quality of the fused image is better than the results of other methods.

Multi-focus image fusion algorithm based on Bandelet and region feature

A multi-focus image fusion algorithm based on Bandelet transform and region statistics was developed,which could fully utilize the second generation Bandelet's advantages of the geometrical regularity of image structure and represent sharp image transitions such as edges efficiently in image fusion source images were firstly decomposed by the Bandelet transform,for reconstructing the fused image,the maximum rule was used to select source images' geometric flow and regional variance was used to the Bandelet coefficients.Finally the fused image was reconstructed by performing the inverse bandelet transform.The experimental results indicate that the Bandelet-based fusion algorithm represents the edge and detailed information well and outperforms the wavelet-based and Laplacian pyramid-based fusion algorithms,especially when the abundant texture and edges are contained in the source images.

基于shearlets的多聚焦图像融合新算法

基于shearlets的多聚焦图像融合新算法

Multi-focus image fusion algorithm based on shearlets

Multi-focus image fusion algorithm based on shearlets

基于shearlets的多聚焦图像融合新算法

Shearlets not only possess all properties that other transforms have, but also are equipped with a rich mathematical structure similar to wavelets, which are associated to a multi-resolution analysis. Recently, shearlets have been used in image denoising, sparse image representation, and edge detection. However, its application in image fusion is still under study. In this letter, we study the feasibility of image fusion using shearlets. Fusion rules of larger high-frequency coefficients based on regional energy, regional variance, and absolute value are proposed because shearlet transform can catch detailed information in any scale and any direction. The fusion accuracy is also further improved by a region consistency check. Several different experiments are adopted to prove that fusion results based on shearlet transform can acquire better fusion quality than any other method.

A Novel DWT Based Multi-focus Image Fusion Method

This paper presents a novel discrete wavelet transform (DWT) based approach for multi-focus image fusion. The method is developed by setting different fusion rules for combining the coefficients of low frequency and high frequency sub-bands separately followed by a consistency verification process. In our method, the coefficients in the low frequency domain are selected based on a maximum sharpness focus measure scheme, while choosing the high frequency sub-bands coefficients, a maximum neighboring energy based fusion scheme is proposed. The performance assessment of the proposed method was conducted and compared with several conventional existing fusion methods. Experimental results can clearly demonstrate that the proposed method is an effective and feasible multi-focus image fusion algorithm.