IR and visible image fusion using DWT and bilateral filter

Singh, Simrandeep; Singh, Harbinder; Gehlot, Anita; Kaur, Jaskirat; Gagandeep, .

doi:10.1007/s00542-022-05315-7

Cited by 24 publications

(4 citation statements)

References 61 publications

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“…Multi-scale transformation is a method that primarily involves the decomposition of the original image into multiple scales, resulting in sub-images at different spatial scales. Common methods for this decomposition include wavelet transforms [ 10 ], pyramid transforms [ 11 ], contourlet transforms (CT) [ 12 ], non-subsampled contourlet transforms (NSCT) [ 13 ], fourth-order partial differential equations (FPDEs) [ 14 ], anisotropic diffusion [ 15 ], and shift-invariant shearlet transforms [ 16 ]. Following this decomposition, pixel-level or region-level fusion strategies are applied, which include techniques such as weight allocation and combination methods like maximum, average, and weighted average.…”

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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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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“…Currently, mainstream visible and SAR image fusion methods can be broadly categorized into two types, including traditional image fusion methods and deep learningbased image fusion methods. Traditional image fusion algorithms mainly include Laplace Pyramid (LP) [1][2][3], Shear Wave (SW) [4][5][6], Discrete Wavelet Transform (DWT) [7][8][9], Non-Subsampled Shearlet Transform (NSST) [10][11][12], Sparse Representation (SR) [13][14][15][16], and other methods. However, traditional methods use complex transformations and manual rules, thus limiting the real-time performance of the algorithms and the integration of semantic information, which restricts their application in advanced visual tasks.…”

Section: Introductionmentioning

confidence: 99%

A Visible and Synthetic Aperture Radar Image Fusion Algorithm Based on a Transformer and a Convolutional Neural Network

Hu,

Su,

Zuo

et al. 2024

Electronics

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For visible and Synthetic Aperture Radar (SAR) image fusion, this paper proposes a visible and SAR image fusion algorithm based on a Transformer and a Convolutional Neural Network (CNN). Firstly, in this paper, the Restormer Block is used to extract cross-modal shallow features. Then, we introduce an improved Transformer–CNN Feature Extractor (TCFE) with a two-branch residual structure. This includes a Transformer branch that introduces the Lite Transformer (LT) and DropKey for extracting global features and a CNN branch that introduces the Convolutional Block Attention Module (CBAM) for extracting local features. Finally, the fused image is output based on global features extracted by the Transformer branch and local features extracted by the CNN branch. The experiments show that the algorithm proposed in this paper can effectively achieve the extraction and fusion of global and local features of visible and SAR images, so that high-quality visible and SAR fusion images can be obtained.

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“…On the contrary, the visible image records the reflected light captured by the sensor and has rich texture details and structure information, so it is in accordance with human visual cognition. The infrared and visible fusion algorithm combines the advantages of both to generate a fused image with prominent targets and abundant texture information, which is widely used in military reconnaissance [ 1 ], industrial production [ 2 ], civilian surveillance [ 3 ], and other fields [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

DSA-Net: Infrared and Visible Image Fusion via Dual-Stream Asymmetric Network

Yin

Yang

Huang

et al. 2023

Sensors

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Infrared and visible image fusion technologies are used to characterize the same scene using diverse modalities. However, most existing deep learning-based fusion methods are designed as symmetric networks, which ignore the differences between modal images and lead to source image information loss during feature extraction. In this paper, we propose a new fusion framework for the different characteristics of infrared and visible images. Specifically, we design a dual-stream asymmetric network with two different feature extraction networks to extract infrared and visible feature maps, respectively. The transformer architecture is introduced in the infrared feature extraction branch, which can force the network to focus on the local features of infrared images while still obtaining their contextual information. The visible feature extraction branch uses residual dense blocks to fully extract the rich background and texture detail information of visible images. In this way, it can provide better infrared targets and visible details for the fused image. Experimental results on multiple datasets indicate that DSA-Net outperforms state-of-the-art methods in both qualitative and quantitative evaluations. In addition, we also apply the fusion results to the target detection task, which indirectly demonstrates the fusion performances of our method.

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IR and visible image fusion using DWT and bilateral filter

Cited by 24 publications

References 61 publications

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

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

A Visible and Synthetic Aperture Radar Image Fusion Algorithm Based on a Transformer and a Convolutional Neural Network

DSA-Net: Infrared and Visible Image Fusion via Dual-Stream Asymmetric Network

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