Detail Enhanced Multi-Exposure Image Fusion Based On Edge Preserving Filters

Singh, Harbinder

doi:10.5565/rev/elcvia.1126

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Figure 10 shows the results of comparing the time performance of our algorithm with other algorithms. From the table, we can see that our algorithm has the best time performance [22][23][24].…”

Section: Experimental Results and Analysismentioning

confidence: 92%

Calculation and Function of Composition Proportion in Photography Based on Digital Information Technology

Feng¹

2022

Mobile Information Systems

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In order to take better pictures, a method of composition proportion in photography based on digital information technology is proposed. Feature extraction is carried out from the captured pictures, and the modular customized design of digital information technology is adopted. The research is carried out in the two fields of image blur and approximate repeated image de redundancy. An improved image blur algorithm based on bag of words (bag of words) kernel function is proposed. The experimental results show that the coarse matching time is the time spent in matching a query image with thousands of images in the database, and the fine matching time is the time spent in matching a query image with eight candidate images with orb. The total query time is about 0.7 seconds, while it takes about 500 seconds to match thousands of images only with orb algorithm. In general, we combine coarse matching and fine matching, which can balance the accuracy and speed well. It is proved that the digital information technology proposed in this paper has a good effect in the calculation and function of composition proportion in photography.

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Section: Experimental Results and Analysismentioning

confidence: 92%

Calculation and Function of Composition Proportion in Photography Based on Digital Information Technology

Feng¹

2022

Mobile Information Systems

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“…Many image fusion techniques aim to condense the dimensionality of the composite image by efficiently transferring crucial information from the input image series to create a seamlessly fused image. 6 MFIF methods based on multi-resolution decomposition (MRD), [7][8][9] sparse representation (SR), 10,11 two-scale decomposition (TSD) 12,13 and deep learning 14,15 are being developed with the advent of image processing techniques. For a recent survey of multifocus image fusion methods see.…”

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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“…The proposed algorithm appropriately enhances the resolution of the image. In [11], the author developed a texture-enhanced multi-exposure image fusion method based on texture features. This method improves the robustness of texture details while avoiding gradient inversion artifacts that may appear in the fused image after DL enhancement.…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Fusion Algorithm in VR Panoramic Image Detail Enhancement Processing

Zhu

2020

IEEE Access

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VR panoramic image is an image imaging technology that covers a wide range of scenes. Its imaging range is much larger than that of traditional imaging systems, and it can fully reflect all the information of the imaging space. However, the current VR panoramic images have the problems that the details are not obvious enough and the processing is not comprehensive enough. In view of the shadow problem in VR panoramic images, this paper proposes a multi-feature fusion VR panoramic image shadow elimination algorithm, which uses HSV color features and LBP / LSFP texture features to obtain shadow detection results, and then obtains the final detection results by fusion. The experimental results prove that while ensuring a low missed detection rate, the false detection rate is greatly reduced. The comprehensive evaluation index Avg in this paper is improved by 3.4% compared with the shadow elimination algorithm based on a single feature. This paper proposes an image saliency detection algorithm and image detail enhancement algorithm based on multi-feature fusion. The final saliency map is obtained through linear fusion. Experiments prove that the image detail enhancement algorithm based on multi-feature fusion mentioned in this paper has achieved very good results. This paper compares the performance of single-feature and multi-feature fusion algorithms. HSV, LBP and LSFP obtain 93.39% accuracy and 0.8708 correlation coefficient. The effect of multi-fusion fusion is better than that of single feature.

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Detail Enhanced Multi-Exposure Image Fusion Based On Edge Preserving Filters

Cited by 6 publications

References 10 publications

Calculation and Function of Composition Proportion in Photography Based on Digital Information Technology

Calculation and Function of Composition Proportion in Photography Based on Digital Information Technology

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

Multi-Feature Fusion Algorithm in VR Panoramic Image Detail Enhancement Processing

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