Efficient image fusion method using improved Bi-dimensional Empirical Mode Decomposition

This paper proposed a novel multiresolution image fusion approach, which is based on bidimensional empirical mode decomposition (BEMD). The BEMD image is different from the former multi-scale image fusion algorithms which need predetermined filters or wavelet functions. This letter made research on several key issues relevant to the 2D image decomposition and proposed a BEMD image fusion rule to 2D Intrinsic Mode Functions (2D IMF) and residue image of BEMD. The experiments showed the improved performance of this novel algorithm compared with the discrete wavelet transform (DWT).

The image fusion plays a crucial role in many fields such as remote sensing, medical and robotics applications. This paper is focused on image fusion of images of different focus depth. The aim is to study these concepts and provide simulations and evaluations on various implementations. When performing image fusion the images are decomposed by bi-dimensional Empirical mode decomposition (BEMD) to obtain high frequency coefficients which is used to determine which parts of the input images that makes it into the fused image. The same technique is tested on images of different modality. In this thesis, a novel bi-dimensional Empirical mode decomposition (BEMD) based image fusion scheme is proposed. The BEMD decomposes the source images into intrinsic mode functions (IMFs) and residual components. IMF components of the first signal in the decomposition of the source images are used to generate the fused images using appropriate fusion rule. Performance evaluation of fused images is done by computing fusion quality metrics and the fusion results are compared with other existing fusion schemes. It is seen that the performance of the proposed scheme is better as compared with the existing fusion schemes.

Youzhi Zheng Tsinghua University, Department of Computer Science and Technology, Beijing, China 100084 Zheng Qin Tsinghua University, School of Software, Beijing, China 100084 We propose a novel multiscale decomposition (MSD) image fusion algorithm, which is region-based image fusion using bidimensional empirical mode decomposition (BEMD). BEMD is a 2-D data-driven decomposition derived from the empirical mode decomposition (EMD), which does not require predetermined filter or wavelet function. The input images are decomposed into a number of intrinsic mode functions (IMFs) as well as a residual image. The fusion is performed region by region based on the segmentations of the input images to produce composite BEMD representation, and then the inverse BEMD transform is applied to obtain the fused image. Experiments show that the proposed image fusion algorithm provides superior performance over traditional fusion schemes in terms of both objective metrics and visual quality.

The interpolation causes the frequency aliasing in intrinsic mode functions (IMF) in bi-dimensional empirical mode decomposition (BEMD). The local orthogonality or the computational complexity of the improved BEMDs can not be met. In this paper, a novel BEMD - local estimation empirical mode decomposition (LEEMD) is proposed. Particularly, the local estimation instead of the interpolation without changing the framework of EMD is proposed to solve the problem. LEEMD will be applied in image fusion based on the proposed background/detail rule. In experiments, the objective and subjective experiments show that LEEMD and its application is effective.

Infrared and visible image fusion based on BEMSD and improved fuzzy set

In hyperspectral remote sensing, achieving high spatial resolution holds paramount importance for an array of applications, such as environmental monitoring, geographic mapping, and precision agriculture. Nevertheless, conventional hyperspectral images frequently grapple with the issue of restricted spatial resolution. We apply optimized inversion methods to hyperspectral image fusion and present an innovative approach for hyperspectral image fusion which combines the Hue–Intensity–Saturation (HIS) transform, the wavelet transform, and the Trust-Region Conjugate Gradient technique. This amalgamation not only refines spatial precision but also augments spectral faithfulness, which is a pivotal aspect for applications like precise object detection and classification. In the context of our investigation, we conducted a thorough validation of our proposed HIS, Wavelet, and Trust-Region Conjugate Gradient (TRCG-HW) method for image fusion using a comprehensive suite of evaluation metrics. These metrics encompassed the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), and Error Relative Global Accuracy Score (ERGAS). The findings incontrovertibly establish TRCG-HW as the preeminent method among those considered. Our study effectively tackles the pressing predicament of low spatial resolution encountered in hyperspectral imaging. This innovative paradigm harbors the potential to revolutionize high-resolution hyperspectral data acquisition, propelling the field of hyperspectral remote sensing forward and efficiently catering to crucial application.

Multi-modality image fusion applied to improve image quality has drawn great attention from researchers in recent years. However, noise is actually generated in images captured by different types of imaging sensors, which can seriously affect the performance of multi-modality image fusion. As the fundamental method of noisy image fusion, source images are denoised first, and then the denoised images are fused. However, image denoising can decrease the sharpness of source images to affect the fusion performance. Additionally, denoising and fusion are processed in separate processing modes, which causes an increase in computation cost. To fuse noisy multi-modality image pairs accurately and efficiently, a multi-modality image simultaneous fusion and denoising method is proposed. In the proposed method, noisy source images are decomposed into cartoon and texture components. Cartoon-texture decomposition not only decomposes source images into detail and structure components for different image fusion schemes, but also isolates image noise from texture components. A Gaussian scale mixture (GSM) based sparse representation model is presented for the denoising and fusion of texture components. A spatial domain fusion rule is applied to cartoon components. The comparative experimental results confirm the proposed simultaneous image denoising and fusion method is superior to the state-of-the-art methods in terms of visual and quantitative evaluations.

There are many image fusion methods that can be used to produce high-resolution multispectral images from a high-resolution panchromatic image and low-resolution multispectral images. Starting from the physical principle of image formation, this paper presents a comprehensive framework, the general image fusion (GIF) method, which makes it possible to categorize, compare, and evaluate the existing image fusion methods. Using the GIF method, it is shown that the pixel values of the high-resolution multispectral images are determined by the corresponding pixel values of the low-resolution panchromatic image, the approximation of the high-resolution panchromatic image at the low-resolution level. Many of the existing image fusion methods, including, but not limited to, intensity-hue-saturation, Brovey transform, principal component analysis, high-pass filtering, high-pass modulation, the a/spl grave/ trous algorithm-based wavelet transform, and multiresolution analysis-based intensity modulation (MRAIM), are evaluated and found to be particular cases of the GIF method. The performance of each image fusion method is theoretically analyzed based on how the corresponding low-resolution panchromatic image is computed and how the modulation coefficients are set. An experiment based on IKONOS images shows that there is consistency between the theoretical analysis and the experimental results and that the MRAIM method synthesizes the images closest to those the corresponding multisensors would observe at the high-resolution level.

Deep learning methods for medical image fusion: A review

X-ray phase contrast imaging (XPCI) can separate the attenuation, refraction, and scattering signals of the object. The application of image fusion enables the concentration of distinctive information into a single image. Some methods have been applied in XPCI field, but wavelet-based decomposition approaches often result in loss of original data. To explore the application value of a novel image fusion method for XPCI system and computed tomography (CT) system. The means of fast adaptive bidimensional empirical mode decomposition (FABEMD) is considered for image decomposition to avoid unnecessary information loss. A parameter δ is proposed to guide the fusion of bidimensional intrinsic mode functions which contain high-frequency information, using a pulse coupled neural network with morphological gradients (MGPCNN). The residual images are fused by the energy attribute fusion strategy. Image preprocessing and enhancement are performed on the result to ensure its quality. The effectiveness of other image fusion methods has been compared, such as discrete wavelet transforms and anisotropic diffusion fusion. The δ-guided FABEMD-MGPCNN method achieved either the first or second position in objective evaluation metrics with biological samples, as compared to other image fusion methods. Moreover, comparisons are made with other fusion methods used for XPCI. Finally, the proposed method applied in CT show expected results to retain the feature information. The proposed δ-guided FABEMD-MGPCNN method shows potential feasibility and superiority over traditional and recent image fusion methods for X-ray differential phase contrast imaging and computed tomography systems.

Image fusion at pixel level without precise registration always causes pseudo colors and other problem. Classification-based fusion scheme can effectively eliminate the false color at the edge of objective. However, the traditional per-pixel classification results in the well-known salt and pepper effect. The only way to smooth the image is to use filters, while impacted on the result of fusion. This paper proposes a method consist of a sequential application of segmentation, classification and fusion techniques. First, the image was multi-resolutely segmented into homogenous areas, and classified it by using the membership functions classifier and additional empirical rules. Subsequently, according to the restriction of the precise classification result, adjusting the multi-spectral image then achieved the fusion by using HSV color transformation. Finally, after compared the statistical properties of the fusion result by different methods, the proposed method showed satisfied result. I. INTRODUCTION With the appearance of plentiful kinds of remote sensing imagery, fusion of satellite images of different spatial resolutions play an important role. Via image fusion, we can obtain more information than can be derived from single kind of image. Pohl prompted that according to the stage which image fusion is performed, there are pixel, feature and decision level. Image fusion at pixel level always was effected by the accuracy of registration, the number of mixed pixel and other factors. The pixel in high-resolution satellite image is smaller sizes and combined with fewer spectral bands that cause greater spectral variation within a class and a greater degree of shadow. Hence, color break and pseudo colors often occur at the edge of object and shadow which fusing the high-resolution image only at pixel levels. During the high-resolution image fusion, for eliminating color break and pseudo colors, using its classification image as the prior knowledge and restricting the fusion area can take full advantage of spectral information from multi-spectral bands and texture information come from pan band to realize the image fusion. It's a method of image fusion that performing at pixel level and integrate with feature level fusion. In this way, phenomenon of pseudo colors can be avoided. In this paper, we propose an image fusion method based on object-oriented classification. Via image segmentation, feature extraction and image fusion based on object-oriented classification, the fusion image can be achieved. In this method, object-oriented classification more appropriate for high-resolution image to get accuracy classification result and overcoming the limit of per-pixel image analyses. Because much information is acquired in the relationship between adjacent pixels, including texture and shape information, which allows for identification of individual objects as oppose to single pixels

The complex bidimensional empirical mode decomposition

In the phenology of pixels, as the end-member proportions of a pixel vary with the progression of seasons, the pixel changes through different versions. In the image fusion of a low spatial resolution multiresolution (MS) pixel, multiple high spatial resolution fused pixels are generated. The original MS pixel and each fused pixel superimpose over different panchromatic (PAN) pixels and have different end-member proportions. Since analogies exist between pixel phenology and image fusion, the spectral change directions of pixels in phenology can be used as a reference to obtain optimal spectral change directions for MS sub-pixels in image fusion. Regarding pixel phenology, it was found that the optimal spectral change direction for an MS sub-pixel in image fusion is along the sub-pixel vector minus a haze vector. Based on this direction and a multivariate regression between the MS and PAN images, we propose a new method for image fusion. In an evaluation using spatially degraded IKONOS MS and PAN images, this method outperforms some selected current image fusion methods.

Hyperspectral image fusion is a key technique of hyperspectral data processing. In recent years, many fusion methods have been proposed, but there is little work concerning evaluation of the performances of different image fusion methods. In this paper, a method called quantitative correlation analysis (QCA) is proposed, which provides a quantitative measure of the information transferred by an image fusion technique into the output image. Using the proposed method, the performances of different image fusion methods can be compared and analyzed directly based on the images of before and after performing the fusion. The correlation information entropy, based on the developed QCA, is also proposed and testified by numerical simulations. Typical hyperspectral data are applied to the proposed method. The results show that the method is effective, and its conclusions agree with the classification results in applications.

Image fusion process is defined as extracting information and specific features from multiple input images and combining them into a single image which has all essential information. Image fusion methods which have been proposed in DCT domain are very convenient and time-saving in real-time applications. The fused output image of existing methods in DCT domain has low quality due to the side effects of their algorithms. In this article, an efficient and simple image fusion method using improved variance criteria is presented. The proposed method uses sharpened input images to make a larger difference between the variance of the corresponding input image's blocks. In addition, by adjusting two threshold values for recognizing the corresponding blocks which have close variance values, the quality of the output image is enhanced significantly. The results of conducting the proposed algorithm with gray-scale multi-focus images and evaluating the performance of the proposed method in comparison with the well-known methods confirm significant improvements in the quality of the output image. Finally, we demonstrate the simplicity of applying our method on color multi-focus images.