A Novel Quality Metric for Image Fusion Based on Color and Structural Similarity

2009 ◽  
Author(s):  
Xiuqiong Zhang
Keyword(s):  
Image Fusion ◽  
Structural Similarity ◽  
Quality Metric

Fused Image Quality Measure Based on Structural Similarity

2011 ◽  
Vol 255-260 ◽  
pp. 2072-2076
Author(s):  
Yi Yong Han ◽  
Jun Ju Zhang ◽  
Ben Kang Chang ◽  
Yi Hui Yuan ◽  
Hui Xu
Keyword(s):  
Image Fusion ◽  
Structural Information ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reference Image ◽  
Human Visual Perception ◽  
Subjective Evaluations ◽  
Fused Image ◽  
Fusion Methods ◽  
Image Quality Measure

Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we present a new approach using structural similarity index for assessing quality in image fusion. The advantages of our measures are that they do not require a reference image and can be easily computed. Numerous simulations demonstrate that our measures are conform to subjective evaluations and can be able to assess different image fusion methods.

scholarly journals Multimodal Medical Image Fusion using Guided Filter in NSCT Domain

2018 ◽  
Vol 11 (4) ◽  
pp. 1937-1946
Author(s):  
Nancy Mehta ◽  
Sumit Budhiraja
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Similarity Index ◽  
Low Frequency ◽  
Structural Similarity ◽  
Relevant Information ◽  
Phase Congruency ◽  
Edge Preserving ◽  
Medical Image Fusion ◽  
Fused Image

Multimodal medical image fusion aims at minimizing the redundancy and collecting the relevant information using the input images acquired from different medical sensors. The main goal is to produce a single fused image having more information and has higher efficiency for medical applications. In this paper modified fusion method has been proposed in which NSCT decomposition is used to decompose the wavelet coefficients obtained after wavelet decomposition. NSCT being multidirectional,shift invariant transform provide better results.Guided filter has been used for the fusion of high frequency coefficients on account of its edge preserving property. Phase congruency is used for the fusion of low frequency coefficients due to its insensitivity to illumination contrast hence making it suitable for medical images. The simulated results show that the proposed technique shows better performance in terms of entropy, structural similarity index, Piella metric. The fusion response of the proposed technique is also compared with other fusion approaches; proving the effectiveness of the obtained fusion results.

Medical Image Fusion in Wavelet and Ridgelet Domains

2017 ◽  
pp. 711-723
Author(s):  
Vikrant Bhateja ◽  
Abhinav Krishn ◽  
Himanshi Patel ◽  
Akanksha Sahu
Keyword(s):  
Image Fusion ◽  
Information Content ◽  
Medical Image ◽  
Similarity Index ◽  
Principal Component ◽  
Structural Similarity ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Multi Scale ◽  
Fused Image

Medical image fusion facilitates the retrieval of complementary information from medical images and has been employed diversely for computer-aided diagnosis of life threatening diseases. Fusion has been performed using various approaches such as Pyramidal, Multi-resolution, multi-scale etc. Each and every approach of fusion depicts only a particular feature (i.e. the information content or the structural properties of an image). Therefore, this paper presents a comparative analysis and evaluation of multi-modal medical image fusion methodologies employing wavelet as a multi-resolution approach and ridgelet as a multi-scale approach. The current work tends to highlight upon the utility of these approaches according to the requirement of features in the fused image. Principal Component Analysis (PCA) based fusion algorithm has been employed in both ridgelet and wavelet domains for purpose of minimisation of redundancies. Simulations have been performed for different sets of MR and CT-scan images taken from ‘The Whole Brain Atlas'. The performance evaluation has been carried out using different parameters of image quality evaluation like: Entropy (E), Fusion Factor (FF), Structural Similarity Index (SSIM) and Edge Strength (QFAB). The outcome of this analysis highlights the trade-off between the retrieval of information content and the morphological details in finally fused image in wavelet and ridgelet domains.

scholarly journals Multi-Modal Medical Image Fusion Based on FusionNet in YIQ Color Space

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1423
Author(s):  
Kai Guo ◽  
Xiongfei Li ◽  
Hongrui Zang ◽  
Tiehu Fan
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Medical Information ◽  
Color Space ◽  
Structural Similarity ◽  
Source Image ◽  
Medical Image Fusion ◽  
Structure Recognition ◽  
Fused Image ◽  
Fusion Framework

In order to obtain the physiological information and key features of source images to the maximum extent, improve the visual effect and clarity of the fused image, and reduce the computation, a multi-modal medical image fusion framework based on feature reuse is proposed. The framework consists of intuitive fuzzy processing (IFP), capture image details network (CIDN), fusion, and decoding. First, the membership function of the image is redefined to remove redundant features and obtain the image with complete features. Then, inspired by DenseNet, we proposed a new encoder to capture all the medical information features in the source image. In the fusion layer, we calculate the weight of each feature graph in the required fusion coefficient according to the trajectory of the feature graph. Finally, the filtered medical information is spliced and decoded to reproduce the required fusion image. In the encoding and image reconstruction networks, the mixed loss function of cross entropy and structural similarity is adopted to greatly reduce the information loss in image fusion. To assess performance, we conducted three sets of experiments on medical images of different grayscales and colors. Experimental results show that the proposed algorithm has advantages not only in detail and structure recognition but also in visual features and time complexity compared with other algorithms.

scholarly journals Implementation of Hybrid Algorithm Based on NSCT for Medical Image Fusion

2019 ◽  
Vol 9 (2S3) ◽  
pp. 86-92
Keyword(s):  
Image Fusion ◽  
Hybrid Algorithm ◽  
Similarity Index ◽  
Structural Similarity ◽  
Single Image ◽  
Medical Field ◽  
Structural Similarity Index ◽  
Positron Emission ◽  
Tumor Recognition ◽  
Multimodal Image Fusion

The process of combining the two different modal images into one single image is multimodal image fusion. The resulting image is helpful in the medical field for effective and better detection of disease and the processing of images; surgery, tumor recognition, illnesses, etc. In the only modes of medical images, the merged image attributes cannot be achieved and can be overcome with the image fusion of various modal images. A new hybrid algorithm for directive multimodal image fusion will be built for this paper based on the non-sub-sampled contourlet transformation. The images will be fuse through the use of the proposed techniques and comparison with existing technological techniques, using quantitative and qualitative measures. MRI and positron-emission tomography (PET) are used. Quantitative steps, like the Entropy (EN) and Structural Similarity Index (SSIM), will be taken to verify the algorithms

scholarly journals Image Registration and Fusion using Moving Frame based Decomposition Framework Algorithm

2021 ◽  
Vol 10 (5) ◽  
pp. 57-63
Author(s):  
Pooja Aspalli ◽  
Prakash Pattan
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Similarity Index ◽  
Structural Similarity ◽  
Moving Frame ◽  
Structural Similarity Index ◽  
Mapping Strategy ◽  
Index Measurement ◽  
Different Types ◽  
Algorithm Implementation

Image fusion is an important process in the medical image diagnostics methods. Fusing images by obtaining information from different source and different types of images(modals) called multi-modal image fusion. This paper implements an effective and fast spatial domain based multimodal image fusion using moving frame based decomposition (MFDF)method. Images from two different modalities are taken and decomposed to texture and approximation components. Weight mapping strategy is applied along with the guide filtering to fuse the approximation components using the final map. Weight mapping using the guide filtering is used for the fusing the images from different modalities. MATLAB is used for algorithm implementation. The results obtained are comparatively competitive with the recent publication[11]. Multi modal image fusion thus implemented gives promising results, when compared to moving frame decomposition framework method. The size and the blurring variable of the guiding filter is optimized to obtain a better Structural Similarity Index Measurement (SSIM).

Structural similarity preserving GAN for infrared and visible image fusion

2020 ◽  
pp. 2050063
Author(s):  
Di Zhang ◽  
Yong Zhou ◽  
Jiaqi Zhao ◽  
Ziyuan Zhou ◽  
Rui Yao
Keyword(s):  
Image Fusion ◽  
Network Architecture ◽  
Information Transfer ◽  
Structural Similarity ◽  
Superior Performance ◽  
Visible Image ◽  
Structure Information ◽  
Multiple Sensors ◽  
Wide Range ◽  
Similarity Preserving

Compared with a single image, in a complex environment, image fusion can utilize the complementary information provided by multiple sensors to significantly improve the image clarity and the information, more accurate, reliable, comprehensive access to target and scene information. It is widely used in military and civil fields, such as remote sensing, medicine, security and other fields. In this paper, we propose an end-to-end fusion framework based on structural similarity preserving GAN (SSP-GAN) to learn a mapping of the fusion tasks for visible and infrared images. Specifically, on the one hand, for making the fusion image natural and conforming to visual habits, structure similarity is introduced to guide the generator network produce abundant texture structure information. On the other hand, to fully take advantage of shallow detail information and deep semantic information for achieving feature reuse, we redesign the network architecture of multi-modal image fusion meticulously. Finally, a wide range of experiments on real infrared and visible TNO dataset and RoadScene dataset prove the superior performance of the proposed approach in terms of accuracy and visual. In particular, compared with the best results of other seven algorithms, our model has improved entropy, edge information transfer factor, multi-scale structural similarity and other evaluation metrics, respectively, by 3.05%, 2.4% and 0.7% on TNO dataset. And our model has also improved by 0.7%, 2.82% and 1.1% on RoadScene dataset.

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