Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Yudan, Liu; Yang, Xiaobo; Zhang, Rongzhu; Albertini, Marcelo Keese; Çelik, Turgay; Jeon, Gwanggil

doi:10.3390/e22010118

Cited by 18 publications

(7 citation statements)

References 42 publications

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“…Entropy [20] states the amount of information included in an image. The better the image fusion, the greater the entropy value.…”

Section: Entropymentioning

confidence: 99%

Infrared and visible image fusion using LatLRR and ResNet

Prema,

Arivazhagan,

Aishwarya

et al. 2024

AIP Conference Proceedings

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Image fusion is a notable concern in the image processing field which helps to produce a resultant image by amalgamating all the features from one or more source images. We envisioned an infrared (IR) and visible (VIS) image fusion technique combining (Latent Low Rank Representation) LatLRR and ResNet to mitigate the loss of contrast, texture, spectral, and spatial details in the resultant image. The fused image seems to be more appropriate and obvious for human and machine vision perception. Using LatLRR, the source images are initially broken down into saliency and latent low rank parts. Then, using weighted-average method, the Latent Low Rank components are integrated. Fusion approach based on deep features is used to overcome the problem of information lost by using RESNET to extract multilayer features for the saliency part as this network has a skip connection which helps to gather information from every layer. After that, the deep features are normalized by nuclear norm, and preliminary weight maps are generated. Combining the original weight maps with a Soft-Max operation yields the final-weight maps. At last, by summing the fused Latent Low Rank component and the Saliency content, the fused image will be reconstructed. Both visual and quantitative performance evaluation is done for the proposed method with five existing methods and it is inferred from the results that the proposed method can well reconstruct the fused image without haloes and artifacts.

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“…Entropy [20] states the amount of information included in an image. The better the image fusion, the greater the entropy value.…”

Section: Entropymentioning

confidence: 99%

Infrared and visible image fusion using LatLRR and ResNet

Prema,

Arivazhagan,

Aishwarya

et al. 2024

AIP Conference Proceedings

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“…Spatial data contain spatial information, and the basis of information measurement lies in the definition of spatial information. Shannon's information theory uses uncertainty as the definition of information and is widely used in urban sprawl analysis [32,33], landslide sensitivity analysis [34], and image processing applications such as band selection [35], image fusion [36,37], classification [38,39], and quality assessment [40]. In addition, Boltzmann entropy, which uses disorder information, has also made significant progress recently [23][24][25][26][27][28][29][30][31].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Feature Framework for Quantifying Information Content of Optical Remote Sensing Imagery

et al. 2022

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Quantifying the information content of remote sensing images is considered to be a fundamental task in quantitative remote sensing. Traditionally, the grayscale entropy designed by Shannon’s information theory cannot capture the spatial structure of images, which has prompted successive proposals of a series of neighborhood-based improvement schemes. However, grayscale or neighborhood-based spatial structure is only a basic feature of the image, and the spatial structure should be divided into the overall structure and the local structure and separately characterized. For this purpose, a multi-feature quantification framework for image information content is proposed. Firstly, the information content of optical remote sensing images is measured based on grayscale, contrast, neighborhood-based topology, and spatial distribution features instead of simple grayscale or spatial structure. Secondly, the entropy metrics of the different features are designed to quantify the uncertainty of images in terms of both pixel and spatial structure. Finally, a weighted model is used to calculate the comprehensive information content of the image. The experimental results confirm that the proposed method can effectively measure the multi-feature information content, including the overall and local spatial structure. Compared with state-of-the-art entropy models, our approach is the first study to systematically consider the multiple features of image information content based on Shannon entropy. It is comparable to existing models in terms of thermodynamic consistency. This work demonstrates the effectiveness of information theory methods in measuring the information content of optical remote sensing images.

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“…In the contribution by Liu et al [ 8 ], “Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter,” an image fusion method using multi-scale decomposition and joint sparse representation is introduced. There are five steps in this work.…”

Section: Themes Of This Special Issuementioning

confidence: 99%