Construction of fused image with improved depth-of-field based on guided co-occurrence filtering

Singh, Harbinder; Sánchez, Carlos Manuel Fernández; Cristóbal, Gabriel

doi:10.1016/j.dsp.2020.102793

Cited by 8 publications

(6 citation statements)

References 37 publications

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“…reshold. Sports training image edge filtering is an image edge detection operator in any direction, which uses lag threshold and NMS to realize image edge filtering [9,10]. It mainly has two parts: calculating image edge response and selecting threshold.…”

Section: Image Edge Filtering Based On Adaptivementioning

confidence: 99%

[Retracted] Research on Sports Training Action Image Recognition Based on SDN

Wang

Shen

2022

Journal of Mathematics

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Current image recognition methods cannot combine the transmission of image data with the interaction of image features, so the steps of image recognition are too independent, and the traditional methods take longer time and cannot complete the image denoising. Therefore, a recognition method of sports training action image based on software defined network (SDN) architecture is proposed. The SDN architecture is used to integrate the image data transmission and interactive process and to optimize the image processing centralization. The network architecture is composed of application layer, control layer, and infrastructure layer. Based on this, the dimension of image sample set is reduced, and the edge detection operator in any direction is constructed. The image edge filter is realized by calculating the response and threshold of image edge by using lag threshold and nonmaximum suppression (NMS). The Hough transform algorithm is improved to optimize the detection range. Extracting the neighborhood feature of sports training action, the recognition of sports training action image based on SDN architecture is completed. Simulation results show that the proposed method takes less time and the image denoising effect is better. In addition, the F1 test results of the proposed method are higher than those of the literature, and the convergence is better. Therefore, the performance of the proposed method is better.

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Section: Image Edge Filtering Based On Adaptivementioning

confidence: 99%

[Retracted] Research on Sports Training Action Image Recognition Based on SDN

Wang

Shen

2022

Journal of Mathematics

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“…However, most researcher already took care of reduction techniques or methods but there is no single technique or method available for the all SAR data according to the feature which can preserve the edge detail without harming spatial resolution. Paper discussed different speckle-noise reduction filters, and the results shown by the tables, figures and output images (Singh et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Urban Features Identification from Dual-Pol SAR Images with Filter Properties

Kumar

2020

Journal of Landscape Ecology

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There is no formal definition of feature identification but it depends on the application and context of the problem. This feature acts as primary elements for execution of several algorithms, hence feature identification is one of the significant steps for has been very interesting for several research groups. Various researchers have attempted in this regard for feature identification. The current work presents an approach for urban feature identification from satellite datasets for a detailed analysis of the features for better management of the resources. Several features based feature extraction approach has been attempted to identify the compare with statistical profiling. Microwave remote sensing is one of the significant methods of remote sensing to get the data where our optical sensors usually failed or less capable to provide accurate and timely sensed data. In today’s world, active remote sensing is one of the greatest technologies which is used widely in many application areas. Synthetic aperture radar is the main object to get the actively remote sensed images. Either it’s optical or microwave data, the satellite images has its many errors, in SAR, while receiving the reflected echoes from the target the trouble has occurred in the form of Speckle Noise in an image. In this paper, the focus is on about the Speckle Noise, SLC & GRD data, the filtered images performance with Boxcar and Median filter, degraded and preserving information of an image, reduce speckle noise effect of an image.

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“…For addressing these constraints, multi-focus image fusion (MFIF) emerges as powerful method for extending DoF. [1][2][3][4][5] By leveraging image processing, MFIF effectively negotiates the trade-off between illumination and DoF. Furthermore, this technique excels in recovering intricate details from specimens featuring complex silica-based cell walls and multi-scale patterns, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…16 Previous research in the realm of MFIF primarily concentrated on crafting and evaluating fusion algorithms tailored for real-world scenarios. Additionally, in studies 2,[17][18][19] MFIF fusion algorithms were introduced to assess their performance on microscopy image datasets. Image fusion is employed in microscopic imaging applications as a post-processing step to enhance the quality of the acquired image.…”

Section: Introductionmentioning

confidence: 99%

An ImageJ plugin for image fusion based on edge-preserving filtering

Singh,

Forero,

Agaoglu

et al. 2024

Optics, Photonics, and Digital Technologies for Imaging Applications VIII

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A novel ImageJ plugin is designed to extend the depth-of-field (DoF) by seamlessly fusing a series of multi-focus images, allowing for in-depth analysis. Moreover, it has been tested on multi-exposure image stacks, demonstrating its adeptness in preserving intricate details within both poorly and brightly illuminated regions of 3-D specimens. The significance of this capability becomes particularly apparent when dealing with images that exhibit a limited DoF and varying exposure settings under low signal-to-noise ratio conditions. The plugin's effectiveness has been thoroughly validated through the processing and analysis of numerous image stacks featuring diverse diatom and cyanobacteria species. The proposed methodology incorporates a two-scale decomposition (TSD) scheme, complemented by the refinement of weight maps using edge-preserving filtering (EPF). This dual approach ensures the preservation of fine details in the fused image while simultaneously minimizing noise. Such innovations make this plugin a valuable tool for researchers and analysts working with complex image datasets.

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Construction of fused image with improved depth-of-field based on guided co-occurrence filtering

Cited by 8 publications

References 37 publications

[Retracted] Research on Sports Training Action Image Recognition Based on SDN

[Retracted] Research on Sports Training Action Image Recognition Based on SDN

Urban Features Identification from Dual-Pol SAR Images with Filter Properties

An ImageJ plugin for image fusion based on edge-preserving filtering

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