Infrared and visible image fusion based on QNSCT and Guided Filter

Yang, Chenxuan; He, Yunan; Sun, Ce; Jiang, Sheng; Li, Ye; Zhao, Peng

doi:10.1016/j.ijleo.2022.168592

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…In order to choose an approach that is appropriate, you need to take into account the target forecasting horizon as well as the time-step, also known as the granularity. Both of these factors are determined by the anticipated use of the forecast [10]. The forecasts for the following day are the primary focus of this research (with a horizon of up to 6 h ahead) [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

Dhanalakshmi¹,

Venkatesh²,

Ranjit

et al. 2022

International Journal of Photoenergy

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In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.

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Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

Dhanalakshmi¹,

Venkatesh²,

Ranjit

et al. 2022

International Journal of Photoenergy

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show abstract

“…Infrared and visible image fusion methods can be broadly divided into two categories: traditional methods and deep learning-based methods [10][11][12][13]. The traditional methods typically accomplish the image fusion goal in the space domain or frequency domain using corresponding mathematical transformations, such as wavelet transform [14], multiscale transform [15,16], sparse representation [17]. However, in the image fusion stage, all these methods require manually designed complex image fusion rules.…”

Section: Introductionmentioning

confidence: 99%

FDNet: An end-to-end fusion decomposition network for infrared and visible images

Di,

Ren,

Liu

et al. 2023

PLoS ONE

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Infrared and visible image fusion can generate a fusion image with clear texture and prominent goals under extreme conditions. This capability is important for all-day climate detection and other tasks. However, most existing fusion methods for extracting features from infrared and visible images are based on convolutional neural networks (CNNs). These methods often fail to make full use of the salient objects and texture features in the raw image, leading to problems such as insufficient texture details and low contrast in the fused images. To this end, we propose an unsupervised end-to-end Fusion Decomposition Network (FDNet) for infrared and visible image fusion. Firstly, we construct a fusion network that extracts gradient and intensity information from raw images, using multi-scale layers, depthwise separable convolution, and improved convolution block attention module (I-CBAM). Secondly, as the FDNet network is based on the gradient and intensity information of the image for feature extraction, gradient and intensity loss are designed accordingly. Intensity loss adopts the improved Frobenius norm to adjust the weighing values between the fused image and the two raw to select more effective information. The gradient loss introduces an adaptive weight block that determines the optimized objective based on the richness of texture information at the pixel scale, ultimately guiding the fused image to generate more abundant texture information. Finally, we design a single and dual channel convolutional layer decomposition network, which keeps the decomposed image as possible with the input raw image, forcing the fused image to contain richer detail information. Compared with various other representative image fusion methods, our proposed method not only has good subjective vision, but also achieves advanced fusion performance in objective evaluation.

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Enhancing infrared images via multi-resolution contrast stretching and adaptive multi-scale detail boosting

Liu

Pan

et al. 2023

Vis Comput

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Infrared and visible image fusion based on QNSCT and Guided Filter

Cited by 7 publications

References 23 publications

Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

FDNet: An end-to-end fusion decomposition network for infrared and visible images

Enhancing infrared images via multi-resolution contrast stretching and adaptive multi-scale detail boosting

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