Gradient-based multi-focus image fusion using foreground and background pattern recognition with weighted anisotropic diffusion filter

Vasu, G. Tirumala; Palanisamy, P.

doi:10.1007/s11760-022-02470-2

Cited by 11 publications

(3 citation statements)

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“…The effects of shadows on object tracking and recognition have historically been a source of difficulty for the shadow detection field in security camera systems. This is often because color-based and gradient-based algorithms are prone to variations in illumination and backdrop brightness Vasu et al (2023), Abro et al (2021). Recent research offers creative solutions incorporating deep learning techniques, especially Convolutional Neural Networks (CNNs), for shadow detection in order to get around these issues Luo et al (2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Shadows in Computer Vision: A MATLAB-Based Approach

Irshad,

Ebrahim,

Abro

et al. 2024

ABBDM

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The abstract outlines a novel MATLAB-based technique for enhancing shadow detection accuracy in computer vision applications. This MATLAB-based method uses thresholding and color analysis to improve the accuracy of shadow identification. Luminance (L*) is distinguished from chromaticity (a* and b*) in digital photographs by converting them into the Lab color space. This makes it easier to identify possible shadowed areas. The luminance channel's thresholding makes it possible to distinguish between regions that are well-lit and those that are shadowed. The computation of chromaticity distance also helps in detecting modest color changes that are suggestive of shadows. The culmination of the suggested technique is the creation of a binary shadow mask, which isolates the image's shaded regions. The research addresses limitations of existing approaches by maintaining robustness in diverse lighting conditions and complex scenarios, thereby enhancing image comprehension and the precision of shadow detection. The proposed technique holds promise for improving object recognition and scene analysis in computer vision applications, offering a robust and reliable solution for identifying and isolating shadows in digital images.

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Section: Literature Reviewmentioning

confidence: 99%

Detecting Shadows in Computer Vision: A MATLAB-Based Approach

Irshad,

Ebrahim,

Abro

et al. 2024

ABBDM

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“…For fusion methods in the spatial domain, such as guided filter [13,14,15], the output of the filter is considered as base layer, the source frame is subtracted from the base layer to obtain the detail layer. However, during the subtraction process, if the pixel value of a region in the source frame is less than the pixel value of the corresponding region in the base layer, the result of the subtraction is a negative number, since the uint8 type of image displays all the negatives as 0, this results in the loss of the features of that region in the detail layer.…”

Section: Introductionmentioning

confidence: 99%

Infrared and visible video fusion method based on inter-frame feature association and PID control

Tnag,

Wang

2024

Preprint

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The purpose of infrared and visible video fusion is to combine the complementary features of videos from different modalities. Most fusion algorithms ignore the feature associations of adjacent frames and the guidance of the source frames to the fusion process. Therefore, this paper proposes a new fusion method. First, a spatial-domain multi-attribute detail feature extraction model is proposed, which is capable of extracting the base layer, the bright detail layer and the dark detail layer of the source frames separately. Then, we propose an associated feature extraction model for adjacent frames, which improves the spatial continuity of the fused video. Furthermore, an exponential homomorphic filter is proposed, which simultaneously increases the dynamic range and contrast of the source infrared frames to obtain a general salient target extraction model. In the feature fusion stage, a weighted fusion rule based on edge intensity is used in the detail layer. Then we design the controller, transfer function and measurement function separately, so as to construct a closed-loop proportional-integral-derivative (PID) control system to fuse the base layer, which ensures that the fused video maintains more information of the source video. Experiments on public datasets demonstrate that our fusion method outperforms some state-of-the-art algorithms. Code: https://github.com/Tang2956/Infrared-and-visible-video-fusion-method-based-on-inter-frame-feature-association-and-PID-control

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“…Due to the limited depth of field of the optical lens, the imaging device sometimes cannot achieve clear focus imaging of all objects or areas in the same scene, resulting in defocus and blurring of the scene content outside the depth of field [ 1 , 2 , 3 , 4 , 5 ]. In order to solve the above problems, multi-focus image fusion technology provides an effective way to synthesize the complementary information contained in multiple partially focused images in the same scene, and then generate an all-in-focus fusion image, which is more suitable for human observation or computer processing, and has wide application value in digital photography, microscopic imaging, holographic imaging, integrated imaging, and other fields [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Sparse Representation-Based Multi-Focus Image Fusion Method via Local Energy in Shearlet Domain

Jia

et al. 2023

Sensors

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Multi-focus image fusion plays an important role in the application of computer vision. In the process of image fusion, there may be blurring and information loss, so it is our goal to obtain high-definition and information-rich fusion images. In this paper, a novel multi-focus image fusion method via local energy and sparse representation in the shearlet domain is proposed. The source images are decomposed into low- and high-frequency sub-bands according to the shearlet transform. The low-frequency sub-bands are fused by sparse representation, and the high-frequency sub-bands are fused by local energy. The inverse shearlet transform is used to reconstruct the fused image. The Lytro dataset with 20 pairs of images is used to verify the proposed method, and 8 state-of-the-art fusion methods and 8 metrics are used for comparison. According to the experimental results, our method can generate good performance for multi-focus image fusion.

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Gradient-based multi-focus image fusion using foreground and background pattern recognition with weighted anisotropic diffusion filter

Cited by 11 publications

References 50 publications

Detecting Shadows in Computer Vision: A MATLAB-Based Approach

Detecting Shadows in Computer Vision: A MATLAB-Based Approach

Infrared and visible video fusion method based on inter-frame feature association and PID control

Sparse Representation-Based Multi-Focus Image Fusion Method via Local Energy in Shearlet Domain

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