scholarly journals Effective Multifocus Image Fusion Based on HVS and BP Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yong Yang ◽  
Wenjuan Zheng ◽  
Shuying Huang
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Fusion Rule ◽  
Subjective Evaluations ◽  
Fused Image ◽  
Fusion Methods ◽  
Opening And Closing ◽  
Image Fusion Method

The aim of multifocus image fusion is to fuse the images taken from the same scene with different focuses to obtain a resultant image with all objects in focus. In this paper, a novel multifocus image fusion method based on human visual system (HVS) and back propagation (BP) neural network is presented. Three features which reflect the clarity of a pixel are firstly extracted and used to train a BP neural network to determine which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Thirdly, the focused regions are detected by measuring the similarity between the source images and the initial fused image followed by morphological opening and closing operations. Finally, the final fused image is obtained by a fusion rule for those focused regions. Experimental results show that the proposed method can provide better performance and outperform several existing popular fusion methods in terms of both objective and subjective evaluations.

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 Infrared and Visible Image Fusion Using Truncated Huber Penalty Function Smoothing and Visual Saliency Based Threshold Optimization

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Chaowei Duan ◽  
Yiliu Liu ◽  
Changda Xing ◽  
Zhisheng Wang
Keyword(s):  
Image Fusion ◽  
Penalty Function ◽  
Visual Saliency ◽  
Fusion Rule ◽  
Visible Image ◽  
Threshold Optimization ◽  
Fused Image ◽  
Fusion Methods ◽  
Visible Images ◽  
Image Representations

An efficient method for the infrared and visible image fusion is presented using truncated Huber penalty function smoothing and visual saliency based threshold optimization. The method merges complementary information from multimodality source images into a more informative composite image in two-scale domain, in which the significant objects/regions are highlighted and rich feature information is preserved. Firstly, source images are decomposed into two-scale image representations, namely, the approximate and residual layers, using truncated Huber penalty function smoothing. Benefiting from the edge- and structure-preserving characteristics, the significant objects and regions in the source images are effectively extracted without halo artifacts around the edges. Secondly, a visual saliency based threshold optimization fusion rule is designed to fuse the approximate layers aiming to highlight the salient targets in infrared images and remain the high-intensity regions in visible images. The sparse representation based fusion rule is adopted to fuse the residual layers with the goal of acquiring rich detail texture information. Finally, combining the fused approximate and residual layers reconstructs the fused image with more natural visual effects. Sufficient experimental results demonstrate that the proposed method can achieve comparable or superior performances compared with several state-of-the-art fusion methods in visual results and objective assessments.

scholarly journals A New Deep Learning Based Multi-Spectral Image Fusion Method

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 570 ◽  
Author(s):  
Jingchun Piao ◽  
Yunfan Chen ◽  
Hyunchul Shin
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Pedestrian Detection ◽  
Fusion Rule ◽  
Activity Level ◽  
Fusion Method ◽  
Multi Scale ◽  
Level Measurement ◽  
Automatic Encoding ◽  
Image Fusion Method

In this paper, we present a new effective infrared (IR) and visible (VIS) image fusion method by using a deep neural network. In our method, a Siamese convolutional neural network (CNN) is applied to automatically generate a weight map which represents the saliency of each pixel for a pair of source images. A CNN plays a role in automatic encoding an image into a feature domain for classification. By applying the proposed method, the key problems in image fusion, which are the activity level measurement and fusion rule design, can be figured out in one shot. The fusion is carried out through the multi-scale image decomposition based on wavelet transform, and the reconstruction result is more perceptual to a human visual system. In addition, the visual qualitative effectiveness of the proposed fusion method is evaluated by comparing pedestrian detection results with other methods, by using the YOLOv3 object detector using a public benchmark dataset. The experimental results show that our proposed method showed competitive results in terms of both quantitative assessment and visual quality.

Color multi-focus image fusion based on transfer learning

2021 ◽  
pp. 1-20
Author(s):  
Yun Wang ◽  
Xin Jin ◽  
Jie Yang ◽  
Qian Jiang ◽  
Yue Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Transfer Learning ◽  
Fusion Method ◽  
Feature Maps ◽  
Initial Decision ◽  
Objective Metrics ◽  
Fused Image ◽  
Focus Image ◽  
Image Fusion Method

Multi-focus image fusion is a technique that integrates the focused areas in a pair or set of source images with the same scene into a fully focused image. Inspired by transfer learning, this paper proposes a novel color multi-focus image fusion method based on deep learning. First, color multi-focus source images are fed into VGG-19 network, and the parameters of convolutional layer of the VGG-19 network are then migrated to a neural network containing multilayer convolutional layers and multilayer skip-connection structures for feature extraction. Second, the initial decision maps are generated using the reconstructed feature maps of a deconvolution module. Third, the initial decision maps are refined and processed to obtain the second decision maps, and then the source images are fused to obtain the initial fused images based on the second decision maps. Finally, the final fused image is produced by comparing the Q ABF metrics of the initial fused images. The experimental results show that the proposed method can effectively improve the segmentation performance of the focused and unfocused areas in the source images, and the generated fused images are superior in both subjective and objective metrics compared with most contrast methods.

scholarly journals GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks

2020 ◽  
Author(s):  
Zhiguang Yang ◽  
Youping Chen ◽  
Zhuliang Le ◽  
Yong Ma
Keyword(s):  
Image Fusion ◽  
Activity Level ◽  
Experimental Result ◽  
Generative Adversarial Networks ◽  
Fusion Method ◽  
Adversarial Networks ◽  
Fused Image ◽  
Fusion Methods ◽  
Image Pairs ◽  
Image Fusion Method

Abstract In this paper, a novel multi-exposure image fusion method based on generative adversarial networks (termed as GANFuse) is presented. Conventional multi-exposure image fusion methods improve their fusion performance by designing sophisticated activity-level measurement and fusion rules. However, these methods have a limited success in complex fusion tasks. Inspired by the recent FusionGAN which firstly utilizes generative adversarial networks (GAN) to fuse infrared and visible images and achieves promising performance, we improve its architecture and customize it in the task of extreme exposure image fusion. To be specific, in order to keep content of extreme exposure image pairs in the fused image, we increase the number of discriminators differentiating between fused image and extreme exposure image pairs. While, a generator network is trained to generate fused images. Through the adversarial relationship between generator and discriminators, the fused image will contain more information from extreme exposure image pairs. Thus, this relationship can realize better performance of fusion. In addition, the method we proposed is an end-to-end and unsupervised learning model, which can avoid designing hand-crafted features and does not require a number of ground truth images for training. We conduct qualitative and quantitative experiments on a public dataset, and the experimental result shows that the proposed model demonstrates better fusion ability than existing multi-exposure image fusion methods in both visual effect and evaluation metrics.

scholarly journals A DWT Based Novel Multimodal Image Fusion Method

2021 ◽  
Vol 38 (3) ◽  
pp. 607-617
Author(s):  
Sumanth Kumar Panguluri ◽  
Laavanya Mohan
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Fusion Rule ◽  
Image Sensor ◽  
Edge Information ◽  
Weighted Fusion ◽  
Fused Image ◽  
Scene Information ◽  
Image Fusion Method ◽  
Multimodal Image Fusion

Nowadays multimodal image fusion has been majorly utilized as an important processing tool in various image related applications. For capturing useful information different sensors have been developed. Mainly such sensors are infrared (IR) image sensor and visible (VI) image sensor. Fusing both these sensors provides better and accurate scene information. The major application areas where this fused image has been mostly used are military, surveillance, and remote sensing. For better identification of targets and to understand overall scene information, the fused image has to provide better contrast and more edge information. This paper introduces a novel multimodal image fusion method mainly for improving contrast and as well as edge information. Primary step of this algorithm is to resize source images. The 3×3 sharpen filter and morphology hat transform are applied separately on resized IR image and VI image. DWT transform has been used to produce "low-frequency" and "high-frequency" sub-bands. "Filters based mean-weighted fusion rule" and "Filters based max-weighted fusion rule" are newly introduced in this algorithm for combining "low-frequency" sub-bands and "high-frequency" sub-bands respectively. Fused image reconstruction is done with IDWT. Proposed method has outperformed and shown improved results in subjective manner and objectively than similar existing techniques.

scholarly journals Image Fusion with Contrast Improving and Feature Preserving

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Din-Chang Tseng ◽  
Yu-Shuo Liu ◽  
Chang-Min Chou
Keyword(s):  
Image Fusion ◽  
Bilateral Filter ◽  
Fusion Method ◽  
Infrared Images ◽  
Significant Information ◽  
Fused Image ◽  
Fusion Methods ◽  
Feature Preserving ◽  
Image Fusion Method ◽  
Previous Image

The goal of image fusion is to obtain a fused image that contains most significant information in all input images which were captured by different sensors from the same scene. In particular, the fusion process should improve the contrast and keep the integrity of significant features from input images. In this paper, we propose a region-based image fusion method to fuse spatially registered visible and infrared images while improving the contrast and preserving the significant features of input images. At first, the proposed method decomposes input images into base layers and detail layers using a bilateral filter. Then the base layers of the input images are segmented into regions. Third, a region-based decision map is proposed to represent the importance of every region. The decision map is obtained by calculating the weights of regions according to the gray-level difference between each region and its neighboring regions in the base layers. At last, the detail layers and the base layers are separately fused by different fusion rules based on the same decision map to generate a final fused image. Experimental results qualitatively and quantitatively demonstrate that the proposed method can improve the contrast of fused images and preserve more features of input images than several previous image fusion methods.

A Novel Multi-Focus Image Fusion Method Using Shearlet Transform

2010 ◽  
Vol 121-122 ◽  
pp. 373-378 ◽  
Author(s):  
Jia Zhao ◽  
Li Lü ◽  
Hui Sun
Keyword(s):  
Image Fusion ◽  
Fusion Rule ◽  
Fusion Method ◽  
Rule Based ◽  
Shearlet Transform ◽  
Fused Image ◽  
Selection Principles ◽  
Focus Image ◽  
Image Fusion Method ◽  
Traditional Image

According to the different frequency areas decomposed by shearlet transform, the selection principles of the lowpass subbands and highpass subbands were discussed respectively. The lowpass subband coefficients of the fused image can be obtained by means of the fusion rule based on the region variation, the highpass subband coefficients can be selected by means of the fusion rule based on the region energy. Experimental results show that comparing with traditional image fusion algorithms, the proposed approach can provide more satisfactory fusion outcome.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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