Fusion of visible and infrared image via compressive sensing using convolutional sparse representation

Nirmalraj, S.; Nagarajan, G.

doi:10.1016/j.icte.2020.11.006

Cited by 18 publications

(4 citation statements)

References 16 publications

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“…Good visual quality, PSNR and SSIM are the results depicted by watermarked host images. The suggested technique's performance is much better and robust against various attacks [16] [31].…”

Section: Junxiu Liu Et Al (July 2019) Recommends a Unique Image Water...mentioning

confidence: 96%

An Improved Hybrid DCT-DWT Blind Watermarking Technique for Securing Multimedia Images

Hema

2022

Preprint

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People are capable of manipulating and copying digital images. Protection and security must be given to the right property from piracy and illegal copies. To safeguard the intellectual property of digital images digital watermarking is used as a solution. Watermarking technique is prevalent due to an effective copyright protection method i.e., only the rightful owner can identify the signature that is embedded in a digital document. In this paper, a blind watermarking scheme is used which is dependent on the image content of certain specific DWT-DCT coefficients. The use of generalized Gaussian distributions helps in modelling the DCT-DWT coefficients of the Source image. The suggested system attained optimal PSNR, MSE and SSIM values and numerous attacks are resisted by an embedded watermark in the image watermarked as per the results of the experiment. Furthermore, the suggested system depicts far more robustness against numerous image processing attacks for example, scaling to 0.25 & 0.50, Cropping, and various types of noise attacks. Experimental results and numerical values of parameters confirms that the hybrid DCT-DWT blind watermarking technique is relatively efficient and also justifies the theoretical analysis.

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“…Good visual quality, PSNR and SSIM are the results depicted by watermarked host images. The suggested technique's performance is much better and robust against various attacks [16] [31].…”

Section: Junxiu Liu Et Al (July 2019) Recommends a Unique Image Water...mentioning

confidence: 96%

An Improved Hybrid DCT-DWT Blind Watermarking Technique for Securing Multimedia Images

Hema

2022

Preprint

View full text Add to dashboard Cite

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“…The fundamental concept of sparse representation (SR) [ 17 , 18 , 19 , 20 , 21 ] posits that image signals can be represented as a linear combination of a select few atoms drawn from a pre-learned dictionary, with the sparse coefficients capturing the salient characteristics of the source images.…”

Section: Introductionmentioning

confidence: 99%

Using Sparse Parts in Fused Information to Enhance Performance in Latent Low-Rank Representation-Based Fusion of Visible and Infrared Images

Hao,

Chen,

Ning

et al. 2024

Sensors

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Latent Low-Rank Representation (LatLRR) has emerged as a prominent approach for fusing visible and infrared images. In this approach, images are decomposed into three fundamental components: the base part, salient part, and sparse part. The aim is to blend the base and salient features to reconstruct images accurately. However, existing methods often focus more on combining the base and salient parts, neglecting the importance of the sparse component, whereas we advocate for the comprehensive inclusion of all three parts generated from LatLRR image decomposition into the image fusion process, a novel proposition introduced in this study. Moreover, the effective integration of Convolutional Neural Network (CNN) technology with LatLRR remains challenging, particularly after the inclusion of sparse parts. This study utilizes fusion strategies involving weighted average, summation, VGG19, and ResNet50 in various combinations to analyze the fusion performance following the introduction of sparse parts. The research findings show a significant enhancement in fusion performance achieved through the inclusion of sparse parts in the fusion process. The suggested fusion strategy involves employing deep learning techniques for fusing both base parts and sparse parts while utilizing a summation strategy for the fusion of salient parts. The findings improve the performance of LatLRR-based methods and offer valuable insights for enhancement, leading to advancements in the field of image fusion.

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“…Brightness enhancement algorithms based on infrared and visible image fusion include multi-scale transform-based algorithms [26,27], sparse representation-based algorithms [28,29], deep learning-based algorithms [30,31], and so on. Its core idea is to integrate the superiority of infrared and visible images by complementing each other, to achieve brightness enhancement.…”

Section: Introductionmentioning

confidence: 99%

Image Quality Enhancement with Applications to Unmanned Aerial Vehicle Obstacle Detection

2022

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Aiming at the problem that obstacle avoidance of unmanned aerial vehicles (UAVs) cannot effectively detect obstacles under low illumination, this research proposes an enhancement algorithm for low-light airborne images, which is based on the camera response model and Retinex theory. Firstly, the mathematical model of low-illumination image enhancement is established, and the relationship between the camera response function (CRF) and brightness transfer function (BTF) is constructed by a common parameter equation. Secondly, to solve the problem that the enhancement algorithm using the camera response model will lead to blurred image details, Retinex theory is introduced into the camera response model to design an enhancement algorithm framework suitable for UAV obstacle avoidance. Thirdly, to shorten the time consumption of the algorithm, an acceleration solver is adopted to calculate the illumination map, and the exposure matrix is further calculated via the illumination map. Additionally, the maximum exposure value is set for low signal-to-noise ratio (SNR) pixels to suppress noise. Finally, a camera response model and exposure matrix are used to adjust the low-light image to obtain an enhanced image. The enhancement experiment for the constructed dataset shows that the proposed algorithm can significantly enhance the brightness of low-illumination images, and is superior to other similar available algorithms in quantitative evaluation metrics. Compared with the illumination enhancement algorithm based on infrared and visible image fusion, the proposed algorithm can achieve illumination enhancement without introducing additional airborne sensors. The obstacle object detection experiment shows that the proposed algorithm can increase the AP (average precision) value by 0.556.

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Fusion of visible and infrared image via compressive sensing using convolutional sparse representation

Cited by 18 publications

References 16 publications

An Improved Hybrid DCT-DWT Blind Watermarking Technique for Securing Multimedia Images

An Improved Hybrid DCT-DWT Blind Watermarking Technique for Securing Multimedia Images

Using Sparse Parts in Fused Information to Enhance Performance in Latent Low-Rank Representation-Based Fusion of Visible and Infrared Images

Image Quality Enhancement with Applications to Unmanned Aerial Vehicle Obstacle Detection

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