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In conventional fusion methods based on NonSubsampled Contourlet Transform (NSCT), low-frequency subband coefficient of an image fails to express sparsely the image's low-frequency information, not in favor of extracting source image features. To address this issue, an infrared and visible image fusion method based on NSCT and joint sparse representation (JSR) was proposed, in which, JSR transform...
In this paper a fuzzy-type-image-fusion technique using hybrid SPIHT and SOMA. This method shows fusion of images and can be used for fusion of multi model image. It is concluded that fusion with advanced single levels offers better fusion quality. This method provides a comparative study between proposed & literature techniques and validation of the projected algorithm as Peak Signal to Noise...
Hyperspectral image classification is a challenging task due to its large dimension. Dimensionality reduction has an important role in hyperspectral image classification as it reduces redundancy by mapping the higher dimensional data into lower dimension without losing any valuable information in the spectral signature of each pixel. In this paper, dimensionality reduction is achieved by band partitioning...
This paper focuses on a image mosaic method based on feature points. Image registration and stitching are the key technology of image mosaic. Based on analysis of research, this paper selected a mosaic algorithm based on SIFT feature points which have a good scale and rotation invariant feature for further research. For the wrong matches because of the algorithm restriction, occlusion, sensor moves,...
In this paper, a novel image stitching method is proposed, which utilizes scale-invariant feature transform (SIFT) feature and single-hidden layer feedforward neural network (SLFN) to get higher precision of parameter estimation. In this method, features are extracted from the image sets by the SIFT descriptor and form into the input vector of the SLFN. The output of the SLFN is those translation,...
The characteristics that non-destructive, wide spectral range, high spectral resolution, and image-spectrum merged make it possible to study colored relics based on hyperspectral imaging. As for mural, the spectral information contained in the hyperspectral image can be used to identify pigments, but the presented fuzzy pattern is hard to provide the intuitive visual effect; on the other hand, the...
Multi-focus image fusion is considered to be a vast research topic. Image fusion is the process in which source images are combined to get a single focused image. This focused image obtained contains relatively more information with all objects in focus and better description of scene. It is applied in various applications like medical imaging, remote sensing etc. Various multi-focus image fusion...
With the recent developments in the field of visual sensor technology, multiple imaging sensors are used in several applications such as surveillance, medical imaging and machine vision, in order to improve their capabilities. The goal of any efficient image fusion algorithm is to combine the visual information, obtained from a number of disparate imaging sensors, into a single fused image without...
Multi-focus image fusion is to extract the focused regions from the multiple images of the same scene and combine them together to produce one fully focused image. The key is to find the focused regions from the source images. In this paper, we transform the problem of finding the focused regions to find the boundaries between the focused and defocused regions in the source images, and propose a novel...
In this paper, a novel region segmentation and sigmoid function based image fusion method is proposed. Different from the traditional fusion approaches limiting to a single fusion strategy, the proposed method is designed with an adaptive multi-strategy fusion rule (AMFR). In our method, the source images are decomposed into low frequency sub bands and high frequency sub bands via the shift-invariant...
The multi-sensor image fusion technology can obtain a more accurate and reliable image to understand the scene or recognize the target more easily. However, most existing algorithms are mainly based on optical images, which are highly susceptible by media interference, and cannot save the textural feature and color information at the same time. In view of these problems, this paper presents a multi-sensor...
The contribution describes newly developed technique used to improve image quality by fusion of information from the multiple images into one resulting image containing better information than each of the input ones. The presented approach is based on the F-Transform, integral transform used to detect gradients, similarity and image fusion, and noise reduction.
Image Fusion is a technique of combining the useful information from a set of images into a single image, where the output fused image will be more informative and useful than any of the input images. Image fusion techniques can improve the quality and increase the application area of these data. This research paper compares the experimental results generated by the proposed SVD based image retrieval...
Integrated circuit (IC) defective image enhancement is very important for the classification and identification of IC real defects. This paper proposes a new algorithm of IC defect image enhancement. Firstly, histogram equalization is used in the IC defective image, which is to improve the contrast of image. Secondly, the image is transformed from RGB to IHS color space, and then space lightness is...
Infrared and visible image fusion, as a powerful tool for the object detection and recognition, has developed with the advent of various imaging modalities. However, resulting images of traditional methods are always difficult to compromise between multimodalities. This paper has solved this problem by a variable-weight fusion rule based on the non-sub sampled contourlet transform (NSCT). The original...
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. In this paper, an algorithm for multi-focus image fusion based on multi-structure top hat operator and image variance is proposed. The fusion process contains the...
In recent years, there has been great progress in the field of multi-focus image fusion. However, existing methods still have their respective defects, and the quality of fused image needs to be improved further. In this paper, a novel fusion method by progressive pixel extraction is proposed. In accordance with the sums of all high-frequency coefficients in a series of windows, the method aims at...
The focus of this work is on improving the recognition performance of low-resolution iris video frames acquired under varying illumination. To facilitate this, an image-level fusion scheme with modest computational requirements is proposed. The proposed algorithm uses the evidence of multiple image frames of the same iris to extract discriminatory information via the Principal Components Transform...
Nighttime imagery poses significant challenges to its enhancement due to loss of color information and limitation of single sensor to capture complete visual information at night. To cope with this challenge, multiple sensors are used to capture reliable nighttime imagery which presents additional demands for reliable visual information fusion. In this paper, we present a system, Scarf, which proposes...
Wetland types of Yellow River Delta are various and serious phenomena of 'same object with different spectrum' and 'different object with same spectrum' is one of the reasons caused low classification accuracy. Combination with multi source images is an efficient method to mitigate this influence. In the paper, principal component transform was carried out to Radarsat four polarization data and the...
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