An infrared and visible image fusion method based on VGG-19 network

Zhou, Jingwen; Ren, Kan; Wan, Minjie; Cheng, Bo; Gu, Guohua; Chen, Qian

doi:10.1016/j.ijleo.2021.168084

Cited by 27 publications

(9 citation statements)

References 16 publications

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“…In this paper, images of the same electronic PCB are combined using X-rays and optical images. Through this process, salient and relevant information from hidden parts of images acquired by an X-ray machine (for instance, inside a chip) is fused with details from optical images [16].…”

Section: Image Fusionmentioning

confidence: 99%

“…The fusion strategy can also be designed with deep learning. In this paper, we employ the innovative fusion method proposed by Jingwen Zhou et al [16] that is presented for infrared and visible image fusion based on the VGG-19 model. In this method, unlike Li et al's proposed approach in [19], the source image does not need to be split into basic and detailed parts.…”

Section: Image Fusionmentioning

confidence: 99%

“…In the i-th layer, N indicates the maximum number of channels. The parameter r represents the size of the average operator and according to [16], it is set to 1. Based on the final activity level map (C i * a ), an initial weight map is calculated by utilizing the SoftMax function.…”

Section: Vgg-19 Network: Features Extraction Processing and Reconstru...mentioning

confidence: 99%

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Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Starodubov,

Danishvar,

Abu Ebayyeh

et al. 2024

Electronics

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Microelectronics and electronic products are integral to our increasingly connected world, facing constant challenges in terms of quality, security, and provenance. As technology advances and becomes more complex, the demand for automated solutions to verify the quality and origin of components assembled on printed circuit boards (PCBs) is skyrocketing. This paper proposes an innovative approach to detecting and classifying microelectronic components with impressive accuracy and reliability, paving the way for a more efficient and safer electronics industry. Our approach introduces significant advancements by integrating optical and X-ray imaging, overcoming the limitations of traditional methods that rely on a single imaging modality. This method uses a novel data fusion technique that enhances feature visibility and detectability across various component types, crucial for densely packed PCBs. By leveraging the WaferCaps capsule network, our system improves spatial hierarchy and dynamic routing capabilities, leading to robust and accurate classifications. We employ decision-level fusion across multiple classifiers trained on different representations—optical, X-ray, and fused images—enhancing accuracy by synergistically combining their predictive strengths. This comprehensive method directly addresses challenges surrounding concurrency, reliability, availability, and resolution in component identification. Through extensive experiments, we demonstrate that our approach not only significantly improves classification metrics but also enhances the learning and identification processes of PCB components, achieving a remarkable total accuracy of 95.2%. Our findings offer a substantial contribution to the ongoing development of reliable and accurate automatic inspection solutions in the electronics manufacturing sector.

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Section: Image Fusionmentioning

confidence: 99%

Section: Image Fusionmentioning

confidence: 99%

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Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Starodubov,

Danishvar,

Abu Ebayyeh

et al. 2024

Electronics

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show abstract

“…First, CNNs are trained on visible, infrared, and fused images to acquire the requisite weightings for fusion [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Second, it leverages pre-trained neural network models to only extract features and obtain weight maps from the images, thereby achieving the fusion objective [ 30 , 31 , 32 , 33 ]; Generative Adversarial Network (GAN)-based methods transform the task of integrating visible and infrared images into an adversarial process, characterized by the interplay between a generator and a discriminator. Their objective is to combine visible and infrared images through the generator, at the same time tasking the discriminator with evaluating the sufficiency of visible and infrared information within the fused image [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ]; Encoder-decoder-based networks consist of two main components: an encoder and a decoder.…”

Section: Introductionmentioning

confidence: 99%

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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Improved Multi-modal Image Fusion with Attention and Dense Networks: Visual and Quantitative Evaluation

Banerjee,

Patra,

Roy

2024

Communications in Computer and Information Science

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An infrared and visible image fusion method based on VGG-19 network

Cited by 27 publications

References 16 publications

Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

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

Improved Multi-modal Image Fusion with Attention and Dense Networks: Visual and Quantitative Evaluation

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