Efficient Image Fusion Research Articles

Infrared and visible image fusion based on robust principal component analysis and compressed sensing

Current infrared and visible image fusion methods do not achieve adequate information extraction, i.e., they cannot extract the target information from infrared images while retaining the background information from visible images. Moreover, most of them have high complexity and are time-consuming. This paper proposes an efficient image fusion framework for infrared and visible images on the basis of robust principal component analysis (RPCA) and compressed sensing (CS). The novel framework consists of three phases. First, RPCA decomposition is applied to the infrared and visible images to obtain their sparse and low-rank components, which represent the salient features and background information of the images, respectively. Second, the sparse and low-rank coefficients are fused by different strategies. On the one hand, the measurements of the sparse coefficients are obtained by the random Gaussian matrix, and they are then fused by the standard deviation (SD) based fusion rule. Next, the fused sparse component is obtained by reconstructing the result of the fused measurement using the fast continuous linearized augmented Lagrangian algorithm (FCLALM). On the other hand, the low-rank coefficients are fused using the max-absolute rule. Subsequently, the fused image is superposed by the fused sparse and low-rank components. For comparison, several popular fusion algorithms are tested experimentally. By comparing the fused results subjectively and objectively, we find that the proposed framework can extract the infrared targets while retaining the background information in the visible images. Thus, it exhibits state-of-the-art performance in terms of both fusion effects and timeliness.

Efficient Landsat image fusion using fuzzy and stationary discrete wavelet transform

ABSTRACTImage fusion is the concept to integrate multiple same scene images while drawing out maximum radiometric information from them by avoiding noise and fictional data. The main objective is to improve the radiometric quality of fused image compared to individual images of the same scene. Existing methods are found to be efficient, but if the similar radiometric information is fused into every image, it produces redundant high frequency of pixels. Therefore, to overcome this issue, in this paper a fuzzy and stationary discrete wavelet transform (FSDWT)-based image fusion technique is proposed. It decomposes Landsat image into stationary values, and then it preserves the radiometric data by using fuzzy if-then rules. In the last phase, FSDWT injects high-frequency blocks from input images and returns a single Landsat image with maximum radiometric data. Quantitative analysis has clearly demonstrated that FSDWT has better structural detail, spatial resolution and spectral information than existing methods.

Multi-focus image fusion using biochemical ion exchange model

An effective and efficient multi-focus image fusion technique is presented for creating a more informative composite image for further human or machine perceptions. The proposed technique is based on a novel theory called biochemical ion exchange model (BIEM) which stems from the mechanism of the mineral nutrient uptake mechanism of the root. The source images with different focuses are considered to be the inputs of BIEM which is proposed in this paper, respectively, and the pixels can be viewed as the ions with different valences and polarities. The spatial frequency is chosen as the determinant of the valence. The final fused image can be obtained under the influence of BIEM and the new SSIM metric. Experimental results and relevant analysis indicate that the proposed fusion technique is promising and has remarked superiorities over other current typical fusion ones.

Medical image fusion by combining parallel features on multi-scale local extrema scheme

Two efficient image fusion algorithms are proposed for constructing a fused image through combining parallel features on multi-scale local extrema scheme. Firstly, the source image is decomposed into a series of smoothed and detailed images at different scales by local extrema scheme. Secondly, the parallel features of edge and color are extracted to get the saliency maps. The edge saliency weighted map aims to preserve the structural information using Canny edge detection operator; Meanwhile, the color saliency weighted map works for extracting the color and luminance information by context-aware operator. Thirdly, the average and weighted average schemes are used as the fusion rules for grouping the coefficients of weighted maps obtained from smoothed and detailed images. Finally, the fused image is reconstructed by the fused smoothed and the fused detailed images. Experimental results demonstrate that the proposed algorithms show the best performances among the other fusion methods in the domain of MRI-CT and MRI-PET fusion.

Multi-focal image fusion using degree of focus and fuzzy logic

The continuous advancement in the field of imaging sensor necessitates the development of an efficient image fusion technique. The multi-focal image fusion extracts the in-focus information from the source images to construct a single composite image with increased depth-of-field. Traditionally, the information in multi-focal images is divided into two categories: in-focus and out-of-focus data. Instead of using a binary focus map, in this work we calculate the degree of focus for each source image using fuzzy logic. The fused image is then generated based on the weighted sum of this information. An initial focus tri-state map is built for each input image by using spatial frequency and a proposed focus measure named as alternate sum modified Laplacian. For the cases where these measures indicate different source images to contain focused pixel or have equal strength, another focus measure based on sum of gradient is employed to calculate the degree of focus in a fuzzy inference system. Finally, the fused image is computed from the weights determined by the degree of focus map of each image. The proposed algorithm is designed to fuse two source images, whereas fusion of multiple input images can be performed by fusing a source image with the fusion output of the previous input group. The comparison of the proposed method with several transform and pixel domain based techniques are conducted in terms of both subjective visual assessment and objective quantitative evaluation. Experimental results demonstrate that our method can be competitive with or even outperforms the methods in comparison.

Efficient image fusion with approximate sparse representation

In this paper, an efficient approximate sparse representation (SR) algorithm with multi-selection strategy is used to solve the image fusion problem. We have shown that the approximate SR is effective for image fusion even if the sparse coefficients are not the sparsest ones possible. A multi-selection strategy is used to accelerate the process of generating the approximate sparse coefficients which are used to guide the fusion of image patches. The relative parameters are also investigated experimentally to further reduce the computational time. The proposed method is compared with some state-of-the-art image fusion approaches on several pairs of multi-source images. The experimental results exhibit that the proposed method is able to yield superior fusion results with less consumption time.

Medical Image Fusion based on Shearlets and Human Feature Visibility

image fusion is a technique that integrates complementary information from multimodality images. The fused image is more suitable for treatment plan strategies. In this paper, an efficient medical image fusion method has been proposed based on shearlet transform and human visibility feature as fusion rule. Image fusion rule is the solution that influences the quality of image fusion. The multimodal medical images were first decomposed using the shearlet transform then fusion rules were applied to shearlet coefficients. The low-frequency coefficients are fused by human visibility feature method. While, the high frequency coefficients are fused by the maximum selection fusion rule. The final fusion image is obtained by directly applying inverse shearlet transform to the fused coefficients. The technique proposed has successfully been used in CT/MRI image fusion for tumor diagnosis. The visual experiments and quantitative assessments demonstrate the effectiveness of this method compared to present image fusion. Keywordshearlet transform; medical image fusion; human visibility feature; multimodality; CT/MRI image.

Multi-focus image fusion using best-so-far ABC strategies

Multi-focus image fusion is a process of combining a set of images that have been captured from the same scene but with different focuses in order to construct an additional sharper image. This process plays an important role in the image processing and machine vision fields. Various algorithms have been developed for this task. The key challenge in the design of multi-focus image fusion algorithms is how to evaluate the local content information of each image from the source images. Simple, but effective, block-based techniques at pixel level are widely used for multi-focus image fusion. However, a fixed block size may not be applicable to every application. A block size that is too small or too large is also not desirable. Hence, optimization of the block size is necessary in order to obtain a fused image that comprises the sharper parts of the source images. Recently, a number of techniques based on evolutionary computation have been applied to block-based multi-focus image fusion. The artificial bee colony (ABC) algorithm is one of the more popular evolutionary computational approaches used to find an optimal solution. In this paper, an efficient and robust block-based multi-focus image fusion method based on the optimal selection of sharper image blocks from source images using best-so-far ABC strategies is proposed. Experiment results show that the proposed method is able to provide good results and outperforms other conventional methods, both visually and quantitatively.

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.

아리랑 2/3호 고해상도 위성영상에 적합한 융합기법

This paper presents an efficient image fusion method to be appropriate for the KOMPSAT- 2 and 3 satellites. The proposed method is based on the well-established component substitution (CS) approach. The proposed method is divided into two parts: 1) The first step is to create a intensity image by the weighted-averaging operation of a multi-spectral (MS) image and 2) the second step is to produce an optimal high-frequency image using the statistical properties of the original MS and panchromatic (PAN) images. The performance of the proposed method is evaluated in both quantitative and visual analysis. Quantitative assessments are performed by using the relative global dimensional synthesis error (Spatial and Spectral ERGAS), the image quality index (Q4), and the spectral angle mapper index (SAM). The qualitative and quantitative assessment results show that the fusion performance of the proposed method is improved in both the spectral and spatial qualities when it is compared with previous CS-based fusion methods.

Multi-Focus Raw Bayer Pattern Image Fusion for Single-Chip Camera

In this paper, an efficient patch-based image fusion approach for raw images of single-chip imaging devices incorporated with the Bayer CFA pattern is presented. The multi-source raw Bayer pattern images are firstly parted into half overlapped patches. Then, the patches with maximum clarity measurement defined for raw Bayer pattern image are selected as the fused patches. Next, all the fused local patches are merged with weighted average method in order to reduce the blockness effect of fused raw Bayer pattern image. Finally, the real color fused image is obtained by gradient based demosaicing technology. The multi-source raw Bayer pattern data is fused before demosaicing, so that the multi-sensor system will be more efficient and the artifacts introduced in demosaicing processing do not accumulate in image fusion processing. For comparison, the raw images are also interpolated firstly, and then various image fusion methods are used to get the fused color images. Experimental results show that the proposed algorithm is valid and very effective.

Fast single image dehazing based on image fusion

Images captured in foggy weather conditions often fade the colors and reduce the contrast of the observed objects. An efficient image fusion method is proposed to remove haze from a single input image. First, the initial medium transmission is estimated based on the dark channel prior. Second, the method adopts an assumption that the degradation level affected by haze of each region is the same, which is similar to the Retinex theory, and uses a simple Gaussian filter to get the coarse medium transmission. Then, pixel-level fusion is achieved between the initial medium transmission and coarse medium transmission. The proposed method can recover a high-quality haze-free image based on the physical model, and the complexity of the proposed method is only a linear function of the number of input image pixels. Experimental results demonstrate that the proposed method can allow a very fast implementation and achieve better restoration for visibility and color fidelity compared to some state-of-the-art methods.

A novel approach for multimodal medical image fusion

Fusion of multimodal medical images increases robustness and enhances accuracy in biomedical research and clinical diagnosis. It attracts much attention over the past decade. In this paper, an efficient multimodal medical image fusion approach based on compressive sensing is presented to fuse computed tomography (CT) and magnetic resonance imaging (MRI) images. The significant sparse coefficients of CT and MRI images are acquired via multi-scale discrete wavelet transform. A proposed weighted fusion rule is utilized to fuse the high frequency coefficients of the source medical images; while the pulse coupled neural networks (PCNN) fusion rule is exploited to fuse the low frequency coefficients. Random Gaussian matrix is used to encode and measure. The fused image is reconstructed via Compressive Sampling Matched Pursuit algorithm (CoSaMP). To show the efficiency of the proposed approach, several comparative experiments are conducted. The results reveal that the proposed approach achieves better fused image quality than the existing state-of-the-art methods. Furthermore, the novel fusion approach has the superiority of high stability, good flexibility and low time consumption.

分级多尺度变换的水下偏振图像融合法

An efficient hierarchical multi-scale image fusion method was proposed.Several polarization parametric images were fused for enhancement by non-negative matrix factorization.A polarization parametric fusion image was obtained.This image contained complete polarization information with low redundancy.Then,with two-dimensional empirical mode decomposition,polarization parameters fusion image and polarization intensity image were decomposed.High-frequency component and low-frequency component were fused by weighted average function.In order to determine fusion weight,exhaustive search method was taken.Finally,the given results of high-frequency sub-images and low-frequency subimages were inverse transformed.The experiments demonstrate that the proposed fusion method has the ability to enhance the details and contrast of polarization image.

Efficient Compressive Multi-Focus Image Fusion

Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed in literatures. However, the traditional clarity measures are not designed for compressive imaging measurements which are maps of source sense with random or likely ran- dom measurements matrix. This paper presents a novel efficient multi-focus image fusion frame- work for compressive imaging sensor network. Here the clarity measure of the raw compressive measurements is not obtained from the random sampling data itself but from the selected Hada- mard coefficients which can also be acquired from compressive imaging system efficiently. Then, the compressive measurements with different images are fused by selecting fusion rule. Finally, the block-based CS which coupled with iterative projection-based reconstruction is used to re- cover the fused image. Experimental results on common used testing data demonstrate the effectiveness of the proposed method.

A Visual Attention Model Based Image Fusion

To develop an efficient image fusion algorithm based on visual attention model for images with distinct objects. Image fusion is a process of combining complementary information from multiple images of the same scene into an image, so that the resultant image contains a more accurate description of the scene than any of the individual source images. The two basic fusion techniques are pixel level and region level fusion. Pixel level fusion deals with the operations on each and every pixel separately. The various pixel level techniques are averaging, stationary wavelet transforms, discrete wavelet transforms, Principal Component Analysis (PCA). But because of less sensitivity to noise and mis-registration, the region level image fusion is an emerging approach in the field of multifocus image fusion. The most appreciated approaches in region-based methods are multifocus image fusion using the concept of focal connectivity and spatial frequency. These two methods works well on still images as well as on video frames as inputs. A new region based technique is been proposed for the multifocus images having distinct objects. The method is based on the visual attention models and results obtained are appreciating for the distinct objects input images. The Proposed method results are highlighted using tenengrade and extended spatial frequency as performance parameters by taking several pairs of multi-focus input images like microscopic images, forensic images and video frames.

Novel multifocus image fusion and reconstruction framework based on compressed sensing

In this study, an efficient multifocus image fusion and reconstruction framework based on compressed sensing in the wavelet domain are proposed. The new framework is composed of three phases. Firstly, the source images are represented with their sparse coefficients using the discrete wavelet transform (DWT). Secondly, the measurements are obtained by the random Gaussian matrix from their sparse coefficients, and are then fused by the proposed adaptive local energy metrics (ALEM) fusion scheme. Finally, a fast continuous linearised augmented Lagrangian method (FCLALM) is proposed to reconstruct the sparse coefficients from the fused measurement, which will be converted by the inverse DWT (IDWT) to the fused image. Our experimental results show that the proposed ALEM image fusion scheme can achieve a higher fusion quality than some existing fusion schemes. In addition, the proposed FCLALM reconstruction algorithm has a higher peak-signal-to-noise ratio and a faster convergence rate as compared with some existing reconstruction methods.

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.

Fusion framework for multi‐focus images based on compressed sensing

In this study, an efficient image fusion framework for multi-focus images is proposed based on compressed sensing. The new fusion framework consists of three parts: image sampling, measurement fusion and image reconstruction. First, the dual-channel pulse coupled neural network model is used in the image sampling part as an important weighting factor in the fusion scheme. Second, the result from the measurement fusion part is reconstructed through a new reconstruction algorithm called self-adaptively modified Landwebber filter. Finally, computer simulation-based experiment is conducted, showing that the novel fusion framework is capable of saving computational resource and enhancing the fusion result and is easy to implement.

An Efficient Image Fusion Scheme based on Nonsubsampled Contourlet Transform

An Efficient Image Fusion Scheme based on Nonsubsampled Contourlet Transform