Multi-resolution Image Fusion Research Articles

Multifocus image fusion using the log-Gabor transform and a Multisize Windows technique

Today, multiresolution (MR) transforms are a widespread tool for image fusion. They decorrelate the image into several scaled and oriented sub-bands, which are usually averaged over a certain neighborhood (window) to obtain a measure of saliency. First, this paper aims to evaluate log-Gabor filters, which have been successfully applied to other image processing tasks, as an appealing candidate for MR image fusion as compared to other wavelet families. Consequently, this paper also sheds further light on appropriate values for MR settings such as the number of orientations, number of scales, overcompleteness and noise robustness. Additionally, we revise the novel Multisize Windows (MW) technique as a general approach for MR frameworks that exploits advantages of different window sizes. For all of these purposes, the proposed techniques are firstly assessed on simulated noisy experiments of multifocus fusion and then on a real microscopy scenario.

Influence of image fusion approaches on classification accuracy: a case study

While many studies in the field of image fusion of remotely sensed data aim towards deriving new algorithms for visual enhancement, there is little research on the influence of image fusion on other applications. One major application in earth science is land cover mapping. The concept of sensors with multiple spatial resolutions provides a potential for image fusion. It minimises errors of geometric alignment and atmospheric or temporal changes. This study focuses on the influence of image fusion on spectral classification algorithms and their accuracy. A Landsat 7 ETM+ image was used, where six multispectral bands (30 m) were fused with the corresponding 15 m panchromatic channel. The fusion methods comprise rather common techniques like Brovey, hue‐saturation‐value transform, and principal component analysis, and more complex approaches, including adaptive image fusion, multisensor multiresolution image fusion technique, and wavelet transformation. Image classification was performed with supervised methods, e.g. maximum likelihood classifier, object‐based classification, and support vector machines. The classification was assessed with test samples, a clump analysis, and techniques accounting for classification errors along land cover boundaries. It was found that the adaptive image fusion approach shows best results with low noise content. It resulted in a major improvement when compared with the reference, especially along object edges. Acceptable results were achieved by wavelet, multisensor multiresolution image fusion, and principal component analysis. Brovey and hue‐saturation‐value image fusion performed poorly and cannot be recommended for classification of fused imagery.

Multiresolution image fusion scheme based on fuzzy region feature

This paper proposes a novel region based image fusion scheme based on multiresolution analysis. The low frequency band of the image multiresolution representation is segmented into important regions, sub-important regions and background regions. Each feature of the regions is used to determine the region’s degree of membership in the multiresolution representation, and then to achieve multiresolution representation of the fusion result. The final image fusion result can be obtained by using the inverse multiresolution transform. Experiments showed that the proposed image fusion method can have better performance than existing image fusion methods.

Spatially adaptive multi-resolution multispectral image fusion

Due to the performance limit of remote sensing systems, multispectral images have limited spatial resolution. Their spatial resolution can be improved by merging them with higher resolution image data. A fundamental problem frequently occurring in existing fusion processes, however, is the distortion of spectral information. This paper presents a spatially adaptive image fusion algorithm which produces visually natural images and retains the quality of local spectral information as well. High frequency information of the high resolution image to be inserted to the resampled multispectral images is controlled by adaptive gains to incorporate the difference of local spectral characteristics between the high and the low resolution images into the fusion. Each gain is estimated to minimize the l 2-norm of the error between the original and the estimated pixel values defined in a spatially adaptive window of which the weights are proportional to the spectral correlation measurements of the corresponding regions. This method is applied to a set of co-registered Landsat 7 Enhanced Thematic Mapper (ETM)+ panchromatic and multispectral image data. The experimental results show that high resolution images can be synthesized by the proposed method, which successfully preserves spectral content of the multispectral images.

A wavelet-based image fusion tutorial

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Different fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or different resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.

A wavelet-based image fusion tutorial

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Different fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or different resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.

Multi-resolution image fusion technique and its application to forensic science

Image fusion is a process of combining two or more images into an image. It can extract features from source images, and provide more information than one image can. Multi-resolution analysis plays an important role in image processing, it provides a technique to decompose an image and extract information from coarse to fine scales. In some practical forensic examinations (such as the cartridge image check), we cannot obtain all information from just one image; on the contrary, we need information from images with difference light sources (or light ways). In this paper, we apply an image fusion method based on multi-resolution analysis to forensic science. Synthetic and real images (such as images from closed-up photography and flash photography) are used to show the capability of the multi-resolution image fusion technique.

A general framework for multiresolution image fusion: from pixels to regions

This paper presents an overview on image fusion techniques using multiresolution decompositions. The aim is twofold: (i) to reframe the multiresolution-based fusion methodology into a common formalism and, within this framework, (ii) to develop a new region-based approach which combines aspects of both object and pixel-level fusion. To this end, we first present a general framework which encompasses most of the existing multiresolution-based fusion schemes and provides freedom to create new ones. Then, we extend this framework to allow a region-based fusion approach. The basic idea is to make a multiresolution segmentation based on all different input images and to use this segmentation to guide the fusion process. Performance assessment is also addressed and future directions and open problems are discussed as well.

Unmixing-based multisensor multiresolution image fusion

Constrained and unconstrained algorithms of the multisensor multiresolution technique (MMT) are discussed. They can be applied to unmix low-resolution images using the information about their pixel composition from co-registered high-resolution images. This makes it possible to fuse the low- and high-resolution images for a synergetic interpretation. The constrained unmixing preserves all the available radiometric information of the low-resolution image. On the other hand, the unconstrained unmixing may be preferable in case of noisy data. An analysis of the MMT sensitivity to sensor errors showed that the strongest requirement is the accuracy of geometric co-registration of the data; the co-registration errors should not exceed 0.1-0.2 of the low-resolution pixel size. Applications of the constrained and unconstrained algorithms are illustrated on examples of unmixing and fusion of the multiresolution reflective and thermal bands of a real TM/LANDSAT image as well as of a simulated image of the future ASTER/EOS-AMI sensor.

Perceptual-based image fusion for hyperspectral data

Three hierarchical multiresolution image fusion techniques are implemented and tested using image data from the Airborne Visual/Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor. The methods presented focus on combining multiple images from the AVIRIS sensor into a smaller subset of images white maintaining the visual information necessary for human analysis. Two of the techniques are published algorithms that were originally designed to combine images from multiple sensors, but are shown to work well on multiple images from the same sensor. The third method presented was developed specifically to fuse hyperspectral images for visual analysis. This new method uses the spatial frequency response (contrast sensitivity) of the human visual system to determine which features in the input images need to be preserved in the composite image(s) thus ensuring the composite image maintains the visually relevant features from each input image. The image fusion algorithms are analyzed using test images with known image characteristics and image data from the AVIRIS hyperspectral sensor. After analyzing the signal-to-noise ratios and visual aesthetics of the fused images, contrast sensitivity based fusion is shown to provide excellent fusion results and, in every case, outperformed the other two methods.

Perceptual-based hyperspectral image fusion using multiresolution analysis

A perceptual-based multiresolution image fusion technique is demonstrated using the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor data. The AVIRIS sensor, which simultaneously collects information in 224 spectral bands that range from 0.4 to 2.5 μm in approximately 10-nm increments, produces 224 images, each representing a single spectral band. The fusion algorithm consists of three stages. First, a Daubechies orthogonal wavelet basis set is used to perform a multiresolution decomposition of each spectral image. Next, the coefficients from each image are combined using a perceptual-based weighting. The weighting of each coefficient, from a given spectral band image, is determined by the spatial-frequency response (contrast sensitivity) of the human visual system. The spectral image with the higher saliency value, where saliency is based on a perceptual energy, will receive the larger weight. Finally, the fused coefficients are used for reconstruction to obtain the fused image. The image fusion algorithm is analyzed using test images with known image characteristics and image data from the AVIRIS hyperspectral sensor. By analyzing the signal-to-noise ratios and visual aesthetics of the fused images, contrast-sensitivity-based fusion is shown to provide excellent fusion results and to outperform previous fusion methods.