A scheme for edge-based multi-focus Color image fusion

Roy, Manali; Mukhopadhyay, Susanta

doi:10.1007/s11042-020-09116-w

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…[ 40 ] for out-of-focused region detection in the saliency space for curve evolution method in LDoF images. Likewise, a multi-focused-fusion technique is proposed by Roy and Mukhopadhyay [ 41 ] for focused edges extraction from the input sample image to achieve a better fusion focused image. They applied zero-cross and Canny operators for edge detectors to obtain a prominent extraction of focused edges.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN

Basar

Waheed

Ali

et al. 2022

Sensors

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The defocus or motion effect in images is one of the main reasons for the blurry regions in digital images. It can affect the image artifacts up to some extent. However, there is a need for automatic defocus segmentation to separate blurred and sharp regions to extract the information about defocus-blur objects in some specific areas, for example, scene enhancement and object detection or recognition in defocus-blur images. The existence of defocus-blur segmentation algorithms is less prominent in noise and also costly for designing metric maps of local clarity. In this research, the authors propose a novel and robust defocus-blur segmentation scheme consisting of a Local Ternary Pattern (LTP) measured alongside Pulse Coupled Neural Network (PCNN) technique. The proposed scheme segments the blur region from blurred fragments in the image scene to resolve the limitations mentioned above of the existing defocus segmentation methods. It is noticed that the extracted fusion of upper and lower patterns of proposed sharpness-measure yields more noticeable results in terms of regions and edges compared to referenced algorithms. Besides, the suggested parameters in the proposed descriptor can be flexible to modify for performing numerous settings. To test the proposed scheme’s effectiveness, it is experimentally compared with eight referenced techniques along with a defocus-blur dataset of 1000 semi blurred images of numerous categories. The model adopted various evaluation metrics comprised of Precision, recall, and F1-Score, which improved the efficiency and accuracy of the proposed scheme. Moreover, the proposed scheme used some other flavors of evaluation parameters, e.g., Accuracy, Matthews Correlation-Coefficient (MCC), Dice-Similarity-Coefficient (DSC), and Specificity for ensuring provable evaluation results. Furthermore, the fuzzy-logic-based ranking approach of Evaluation Based on Distance from Average Solution (EDAS) module is also observed in the promising integrity analysis of the defocus blur segmentation and also in minimizing the time complexity.

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Section: Literature Reviewmentioning

confidence: 99%

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN

Basar

Waheed

Ali

et al. 2022

Sensors

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“…Similarly, Fahimuddin et al [76] introduced a multi-scale re-blurring technique for the detection of the focused region in the saliency space and aimed at curve evolution using LDOF images. Manali et al [77] proposed a multi-focus regionbased color image fusion that extracts the focused edges from the input image to obtain a better-focused fusion image. They adopted Zero-cross and Canny edge detector operators for focused edge extractions.…”

Section: A Defocused Image Segmentation and Pcnn Algorithmmentioning

confidence: 99%

A Novel Defocused Image Segmentation Method Based on PCNN and LBP

et al. 2021

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The defocus blur concept adds an artistic effect and enables an enhancement in the visualization of image scenery. Moreover, some specialized computer vision fields, such as object recognition or scene restoration enhancement, might need to perform segmentation to separate the blurred and nonblurred regions in partially blurred images. This study proposes a sharpness measure comprised of a Local Binary Pattern (LBP) descriptor and Pulse Coupled Neural Network (PCNN) component used to implement a robust approach for segmenting in-focus regions from out of focus sections in the scene. The proposed approach is very robust in the sense that the parameters of the model can be modified to accommodate different settings. The presented metric exploits the fact that, in general, local patches of the image in blurry regions have less prominent LBP descriptors than non-blurry regions. The proposed approach combines this sharpness measure with the PCNN algorithm; the images are segmented along with clear regions and edges of segmented objects. The proposed approach has been tested on a dataset comprised of 1000 defocused images with eight state-of-the-art methods. Based on a set of evaluation metrics, i.e., precision, recall, and F1-Measure, the results show that the proposed algorithm outperforms previous works in terms of prominent accuracy and efficiency improvement. The proposed approach also uses other evaluation parameters, i.e., Accuracy, Matthews Correlation Coefficient (MCC), Dice Similarity Coefficient (DSC), and Specificity, for providing a better assessment of the results obtained by our proposal. Moreover, we adopted a fuzzy logic ranking scheme inspired by the Evaluation Based on Distance from Average Solution (EDAS) technique to interpret the defocus segmentation integrity. The experimental outputs illustrate that the proposed approach outperforms the referenced methods by optimizing the segmentation quality and reducing the computational complexity.

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“…The method proposed in this paper is not limited to grayscale images, but also supports an extension to the fusion of color images because a color image contains three channels, namely R, G and B channels [25]. And each layer of channels is regarded as a separate one.…”

Section: Fusion Of Color Imagesmentioning

confidence: 99%

A Fast Fusion Method for Visible and Infrared Images Using Fourier Transform and Difference Minimization

2020

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Images of different modalities play important roles in the fields of military, navigation, and target detection, including visible and infrared images. Existing fusion methods can reach relatively good fusion effects, but often make the processing speed slow. To achieve the purpose of faster fusion, this paper proposes a fast fusion of visible and infrared images (FFVI) based on Fourier transform and difference minimization. First, both visible and infrared images are transformed using Fourier transform, and the contour information of infrared image is extracted out at the same time. Then the two transformed images are added together to obtain a composite image, the inverse Fourier transform and the grayscale normalization are performed on the composite transformation image, which will generate an image that reflects the target features. Finally, the resulting image is reconstructed by fusing visible and the generated images according to the principle of "difference minimization". The experimental results show that the novel method FFVI proposed in this paper can attain remarkable fusion effects in terms of the subjective qualitative and the objective quantitative analysis. In addition, this method outperforms several representative image fusion algorithms in the computational speed, which will be beneficial to practical application.

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A scheme for edge-based multi-focus Color image fusion

Cited by 6 publications

References 37 publications

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN

A Novel Defocused Image Segmentation Method Based on PCNN and LBP

A Fast Fusion Method for Visible and Infrared Images Using Fourier Transform and Difference Minimization

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