Multi-focus Image Fusion Scheme Research Articles

Fusion of full-field optical angiography images via gradient feature detection

Full-field optical angiography (FFOA)—a real-time non-invasive imaging technique for extracting biological blood microcirculation information—contributes to an in-depth understanding of the functional and pathological changes of biological tissues. However, owing to the limitation of the depth-of-field (DOF) of optical lenses, existing FFOA imaging methods cannot capture an image containing every blood-flow information. To address this problem, this study develops a long-DOF full-field optical angiography imaging system and proposes a novel multi-focus image fusion scheme to expand the DOF. First, FFOA images with different focal lengths are acquired by the absorption intensity fluctuation modulation effect. Second, an image fusion scheme based on gradient feature detection in a nonsubsampled contourlet transform domain is developed to capture focus features from FFOA images and synthesize an all-focused image. Specifically, FFOA images are decomposed by NSCT into coefficients and low-frequency difference images; thereafter, two gradient feature detection-based fusion rules are used to select the pre-fused coefficients. The experimental results of both phantom and animal cases show that the proposed fusion method can effectively extend the DOF and address practical FFOA image defocusing problems. The fused FFOA image can provide a more comprehensive description of blood information than a single FFOA image.

A Multifocus Image Fusion Scheme Based on Similarity Measure of Transformed Isosceles Triangles Between Intuitionistic Fuzzy Sets

Intuitionistic fuzzy set (IFS) theory is widely used to solve imprecise and uncertain problems, and the similarity measure between IFSs can describe the similarity or dissimilarity degree of objects in the real word. In image fusion, it is a key issue to measure and extract the features of multifocus images to fuse them. This research proposes a similarity measure technique of IFSs, which is introduced into image fusion to measure the feature similarity of adjacent pixels. In this work, we present an available similarity measure method between IFSs and propose an image fusion method based on this measure and also design an edge reserved method based on the Canny edge detection to reinforce the image fusion effect. First, the proposed similarity measure is introduced and proved. Second, a focus detection method that can detect the focused regions of an image is designed based on the proposed measure method of IFSs to obtain a preliminary decision map. Third, morphological operations are employed to generate the secondary decision map. Fourth, a Canny edge model-based technique is designed to preserve the edges of the source images. Extensive experiments show that the presented similarity measure method of IFSs is reliable, and the fuzzy measure-based image fusion scheme is effective compared with current methods.

Multifocus image fusion scheme based on discrete cosine transform and spatial frequency

Multifocus images are different images of the same scene captured with different focus in the cameras. These images when considered individually may not give good quality. Hence to obtain a good quality image, this work proposes an algorithm for fusing multifocus images using Discrete Cosine Transform and spatial frequency. The proposed algorithm works for fusing any number of images. The second step calculates the average and maximum of all the source images and reduces the source images to be processed as two. Then Discrete Cosine Transform (DCT) is applied over the two input images. Min-Max normalization is done on the DCT coefficients and fusion is done using spatial frequency. Inclusion of the second step of the proposed algorithm in some existing algorithms such as Stationary Wavelet Transform, Principal Component Analysis and spatial fusion improves the performance. The metrics used for evaluation proves that the proposed algorithm gives better results than the other algorithms using DCT and state of the art techniques.

Energy Efficient Compressive Sensing based Multi-focus Image Fusion Scheme for WVSN

Energy Efficient Compressive Sensing based Multi-focus Image Fusion Scheme for WVSN

A lightweight scheme for multi-focus image fusion

The aim of multi-focus image fusion is to fuse the images taken from the same scene with different focuses so that we can obtain a resultant image with all objects in focus. However, the most existing techniques in many cases cannot gain good fusion performance and acceptable complexity simultaneously. In order to improve image fusion efficiency and performance, we propose a lightweight multi-focus image fusion scheme based on Laplacian pyramid transform (LPT) and adaptive pulse coupled neural networks-local spatial frequency (PCNN-LSF), and it only needs to deal with fewer sub-images than common methods. The proposed scheme employs LPT to decompose a source image into the corresponding constituent sub-images. Spatial frequency (SF) is calculated to adjust the linking strength β of PCNN according to the gradient features of the sub-images. Then oscillation frequency graph (OFG) of the sub-images is generated by PCNN model. Local spatial frequency (LSF) of the OFG is calculated as the key step to fuse the sub-images. Incorporating LSF of the OFG into the fusion scheme (LSF of the OFG represents the information of its regional features); it can effectively describe the detailed information of the sub-images. LSF can enhance the features of OFG and makes it easy to extract high quality coefficient of the sub-image. The experiments indicate that the proposed scheme achieves good fusion effect and is more efficient than other commonly used image fusion algorithms.

A Novel Multi-Focus Image Fusion Method Based on Stationary Wavelet Transform and Local Features of Fuzzy Sets

The key issue of multi-sensor image fusion is how to accurately extract and fuse the high-quality pixels or coefficients of source images. Nevertheless, the so-called high-quality is an uncertain or fuzzy definition, which is very suitable for fuzzy theory to address this problem. By the integration of stationary wavelet transform (SWT) and fuzzy sets, this paper proposes a new multi-focus image fusion scheme, which can merge the important features of different source images into a fused image. First, the source images are decomposed by SWT to get a set of sub-images with different detailed features. Second, the Gaussian membership function (GMF) is utilized to get the fuzzy sets of sub-images data. Third, the local spatial frequency (LSF) is employed to extract the local features of the sub-images by the generated fuzzy sets. At last, the fusion rule is designed based on consistency verification to fuse the sub-images according to the LSF of fuzzy sets, and then inverse SWT (ISWT) is implemented to reconstruct the fused image. The experimental and contrastive results of different image sets show that the proposed method is an effective multi-focus image fusion scheme which can achieve better fusion effect than other methods.

Fusion of multi-focus images with registration inaccuracies

Image fusion combines complementary information for several input images. To obtain useful information from two misaligned images, registration is required. A hybrid textural registration-based multi-focus image fusion scheme is proposed. The Gabor filtering with specific frequency and orientation is used to extract different texture features from the image. The resulting Gabor-filtered images are then aligned using existing affine transformation. The proposed registration scheme yields superior performance as compared to affine registration, as Gabor transform extracts all the features. The fusion is performed using undecimated dual-tree complex wavelet transform. The quantitative and qualitative analysis of the proposed scheme outperforms existing image fusion schemes.

Multifocus image fusion scheme based on the multiscale curvature in nonsubsampled contourlet transform domain

An efficient multifocus image fusion scheme in nonsubsampled contourlet transform (NSCT) domain is proposed. Based on the property of optical imaging and the theory of defocused image, we present a selection principle for lowpass frequency coefficients and also investigate the connection between a low-frequency image and the defocused image. Generally, the NSCT algorithm decomposes detail image information indwells in different scales and different directions in the bandpass subband coefficient. In order to correctly pick out the prefused bandpass directional coefficients, we introduce multiscale curvature, which not only inherits the advantages of windows with different sizes, but also correctly recognizes the focused pixels from source images, and then develop a new fusion scheme of the bandpass subband coefficients. The fused image can be obtained by inverse NSCT with the different fused coefficients. Several multifocus image fusion methods are compared with the proposed scheme. The experimental results clearly indicate the validity and superiority of the proposed scheme in terms of both the visual qualities and the quantitative evaluation.

Multi-focus Image Fusion with Sparse Feature Based Pulse Coupled Neural Network

In order to better extract the focused regions and effectively improve the quality of the fused image, a novel multi-focus image fusion scheme with sparse feature based p ulse c oupled n eural n etwork ( PCNN ) is proposed. The registered source images are decomposed into principal matrices and sparse matrices by r obust p rincipal c omponent a nalysis ( RPCA ). T he salient features of the sparse matrices construct the sparse feature space of the source images. The sparse features are used to motivate the PCNN neurons. The focused regions of the source images are detected by the output of the PCNN and integrat ed to construct the final fused image. Experimental results show that the proposed scheme works better in extracting the focused regions and improving the fusion quality compared to the other existing fusion methods in both spatial and transform domain.

Multifocus image fusion using focus measure of fractional differential and NSCT

In this paper, a new multifocus image fusion scheme based on fractional differential and NSCT is proposed. Firstly, in virtue of the properties of fractional differential, a novel focus measure in nonsubsampled contourlet transform (NSCT) domain is presented and used to determine which coefficient is from the focused region. Then, based on the imaging principle of the multifocus image and the focus measure, a new selection principle for the lowpass subbands coefficients is developed. Meanwhile, focus measure maximum choosing scheme, namely select the coefficient with maximum focus measure value as the corresponding coefficient of the fused image, is applied to the high-frequency subbands. Finally, the inverse NSCT is employed to reconstruct the fused image and a pleasing fused result is generated. The experimental results show that the proposed method outperforms the conventional multifocus image fusion methods in both subjective and objective qualities.

Multi-focus image fusion based on robust principal component analysis and pulse-coupled neural network

Multi-focus image fusion combines multiple source images with different focus points into one image, so that the resulting image appears all in-focus. In order to improve the accuracy of focused region detection and fusion quality, a novel multi-focus image fusion scheme based on robust principal component analysis (RPCA) and pulse-coupled neural network (PCNN) is proposed. In this method, registered source images are decomposed into principal component matrices and sparse matrices with RPCA decomposition. The local sparse features computed from the sparse matrix construct a composite feature space to represent the important information from the source images, which become inputs to PCNN to motivate the PCNN neurons. The focused regions of the source images are detected by the firing maps of PCNN and are integrated to construct the final, fused image. Experimental results demonstrate that the superiority of the proposed scheme over existing methods and highlight the expediency and suitability of the proposed method.

Multi-focus image fusion based on non-negative matrix factorization and difference images

Multi-focus image fusion means to fuse multiple source images with different focus settings into one image, so that the resulting image appears sharper. One of the keys to image fusion is how to represent the source images effectively and completely. To address this problem, in this study a novel multi-focus image fusion scheme based on non-negative matrix factorization (NMF) and difference images is proposed. The temporary fused image is constructed by fusing the registered source images with NMF. The focused regions of the source images are detected by the salient features of the difference images between the temporary fused image and source images. The final fused image is produced by combining the focused regions. The experimental results demonstrate that the proposed method is capable of efficiently representing the source images and significantly improving the fusion quality compared to the other existing fusion methods, in terms of visual and quantitative evaluations.

Discrete Cosine Transform based fusion of multi-focus images for visual sensor networks

This paper presents a simple and efficient multi-focus image fusion scheme explicitly designed for wireless visual sensor systems equipped with resource constrained, battery powered image sensors employed in surveillance, hazardous environment like battlefields etc. Here the fusion of multi-focus images is based on higher valued Alternating Current (AC) coefficients calculated in Discrete Cosine Transform (DCT) domain. The proposed method overcomes the computation and energy limitation of low power devices and is investigated in terms of image quality and computation energy. Simulations are performed using Atmel Atmega128 processor of Mica 2 mote to measure the resultant energy savings. The experimental results verify the significant efficiency improvement of the proposed method in output quality and energy consumption, when compared with other fusion techniques in DCT domain.

A new fusion scheme for multifocus images based on focused pixels detection

In this paper, a new multifocus image fusion scheme based on the technique of focused pixels detection is proposed. First, a new improved multiscale Top-Hat (MTH) transform, which is more effective than the traditional Top-Hat transform in extracting focus information, is introduced and utilized to detect the pixels of the focused regions. Second, the initial decision map of the source images is generated by comparing the improved MTH value of each pixel. Then, the isolated regions removal method is developed and employed to refine the initial decision map. In order to improve the quality of the fused image and avoid the discontinuity in the transition zone, a dual sliding window technique and a fusion strategy based on multiscale transform are developed to achieve the transition zones fusion. Finally, the decision maps of the focused regions and the transition zones are both used to guide the fusion process, and then the final fused image is formed. The experimental results show that the proposed method outperforms the conventional multifocus image fusion methods in both subjective and objective qualities.

Multifocus image fusion scheme based on feature contrast in the lifting stationary wavelet domain

Abstract For fusion of multifocus images, a novel image fusion method based on multiscale products in lifting stationary wavelet (LSWT) domain is proposed in this article. In order to avoid the influence of noise and select the coefficients of the fused image properly, different subband coefficients employ different selection principles. For choosing the low frequency subband coefficients, a new modified energy of Laplacian (EOL) is proposed and used as the focus measure to select the coefficients from the clear parts of the low frequency subband images; when choosing the high frequency subband coefficients, a novel feature contrast measurement of the multiscale products is proposed, which is proved to be more suitable for fusion of multifocus images than the traditional contrast measurement, and used to select coefficients from the sharpness parts of the high frequency subbands. Experimental results demonstrate that the proposed fusion approach outperforms the traditional discrete wavelet transform (DWT)-based, LSWT-based and LSWT-traditional-contrast-(LSWT-Tra-Con)-based image fusion methods, even though the source images are corrupted by a Gaussian noise, in terms of both visual quality and objective evaluation.

Multifocus image fusion scheme using focused region detection and multiresolution

To solve the fusion problem of the multifocus images of the same scene, a novel algorithm based on focused region detection and multiresolution is proposed. In order to integrate the advantages of spatial domain-based fusion methods and transformed domain-based fusion methods, we use a technique of focused region detection and a new fusion method of multiscale transform (MST) to guide pixel combination. Firstly, the initial fused image is acquired with a novel multiresolution image fusion method. The pixels of the original images, which are similar to the corresponding initial fused image pixels, are considered to be located in the sharply focused regions. By this method, the initial focused regions can be determined, and the techniques of morphological opening and closing are employed for post-processing. Then the pixels within the focused regions in each source image are selected as the pixels of the fused image; meanwhile, the initial fused image pixels which are located at the focused border regions are retained as the pixels of the final fused image. The fused image is then obtained. The experimental results show that the proposed fusion approach is effective and performs better in fusing multi-focus images than some current methods.

Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain

Multifocus image fusion aims at overcoming imaging cameras's finite depth of field by combining information from multiple images with the same scene. For the fusion problem of the multifocus image of the same scene, a novel algorithm is proposed based on multiscale products of the lifting stationary wavelet transform (LSWT) and the improved pulse coupled neural network (PCNN), where the linking strength of each neuron can be chosen adaptively. In order to select the coefficients of the fused image properly with the source multifocus images in a noisy environment, the selection principles of the low frequency subband coefficients and bandpass subband coefficients are discussed, respectively. For choosing the low frequency subband coefficients, a new sum modified-Laplacian (NSML) of the low frequency subband, which can effectively represent the salient features and sharp boundaries of the image in the LSWT domain, is an input to motivate the PCNN neurons; when choosing the high frequency subband coefficients, a novel local neighborhood sum of Laplacian of multiscale products is developed and taken as one type of feature of high frequency to motivate the PCNN neurons. The coefficients in the LSWT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed fusion approach outperforms the traditional discrete wavelet transform (DWT)-based, LSWT-based and LSWT–PCNN-based image fusion methods even though the source image is in a noisy environment in terms of both visual quality and objective evaluation.