A Hybrid Multi-focus Image Fusion Technique using SWT and PCA

Tyagi, Tushar; Gupta, Parth; Singh, Prabhishek

doi:10.1109/confluence47617.2020.9057960

Cited by 20 publications

(4 citation statements)

References 15 publications

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“…Other areas where it is applied effectively is image fusion. [48][49][50][51] The method noise approach is mostly employed as a post-processing step. It is used to recover the filtered image's unprocessed/unfiltered components.…”

Section: Methods Noise Thresholding In Nsct Domainmentioning

confidence: 99%

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Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Singh

Diwakar

2023

Int J Imaging Syst Tech

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Medical diagnostics rely heavily on ultrasound imaging since it is non-invasive, inexpensive, and able to provide images in real time. However, the inherent existence of a kind of signal-dependent noise in these images diminishes the use of ultrasound imaging systems. Speckle noise is inherently present in ultrasound images. Its inherent presence occurs during the image acquisition phase. It decreases the diagnostic usefulness of this imaging modality since it interferes with and reduces the image resolution and contrast. Speckle noise is multiplicative in nature. This multiplicative nature of speckle noise causes adverse effects on ultrasound imaging because it distorts the image quality and causes a loss of the patient's informative content in ultrasound imaging. This causes difficulty in tissue characterization. A lot of research has been done in this field to remove speckle noise while preserving medical information in the image. To handle this issue, research has been grouped into multiple domains. The two main domains include homomorphic and non-homomorphic filtering. The homomorphic filtering uses a logarithmic transform that converts this multiplicative nature into an additive nature and uses any additive image restoration model to do a despeckling task. Non-homomorphic filtering encompasses all methods that do not employ a logarithmic transform. Since the presence of speckle noise in ultrasound imaging is an inherent property. Therefore, the despeckling of medical ultrasound images is a mandatory task that cannot be avoided or ignored. This paper proposes a two-step hybrid and homomorphic despeckling technique, where modified total variation method is applied as step one for speckle reduction purposes. Step two implements method of noise thresholding in the non-subsampled contourlet transform (NSCT) domain using the bivariate shrinkage rule for edge preservation. The effectiveness and reliability of the proposed method are tested by comparing it with some of the latest methods based on qualitative and quantitative analyses. This paper will help the new researchers understand various problems and their solutions related to ultrasound image despeckling.

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Section: Methods Noise Thresholding In Nsct Domainmentioning

confidence: 99%

“…The idea of method noise is employed in different fields of image restoration like digital image denoising, medical image denoising, and SAR image despeckling. Other areas where it is applied effectively is image fusion 48–51 . The method noise approach is mostly employed as a post‐processing step.…”

Section: Literature Surveymentioning

confidence: 99%

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Singh

Diwakar

2023

Int J Imaging Syst Tech

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“…Spatial domain, transform domain, and their combination make up traditional medical image fusion algorithms. Principal component analysis 23 is a common fusion technique for medical imaging based on the spatial domain. Nevertheless, spectral and spatial distortion of the merged images are produced by spatial domain approaches.…”

Section: Related Workmentioning

confidence: 99%

Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation

Cheng

Wang

2023

Sci Rep

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Medical image segmentation provides various effective methods for accuracy and robustness of organ segmentation, lesion detection, and classification. Medical images have fixed structures, simple semantics, and diverse details, and thus fusing rich multi-scale features can augment segmentation accuracy. Given that the density of diseased tissue may be comparable to that of surrounding normal tissue, both global and local information are critical for segmentation results. Therefore, considering the importance of multi-scale, global, and local information, in this paper, we propose the dynamic hierarchical multi-scale fusion network with axial mlp (multilayer perceptron) (DHMF-MLP), which integrates the proposed hierarchical multi-scale fusion (HMSF) module. Specifically, HMSF not only reduces the loss of detail information by integrating the features of each stage of the encoder, but also has different receptive fields, thereby improving the segmentation results for small lesions and multi-lesion regions. In HMSF, we not only propose the adaptive attention mechanism (ASAM) to adaptively adjust the semantic conflicts arising during the fusion process but also introduce Axial-mlp to improve the global modeling capability of the network. Extensive experiments on public datasets confirm the excellent performance of our proposed DHMF-MLP. In particular, on the BUSI, ISIC 2018, and GlaS datasets, IoU reaches 70.65%, 83.46%, and 87.04%, respectively.

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“…In 2018, Siddique et al [25] proposed an image fusion method based on color-principal component analysis (C-PCA), which was divided into three stages: first, color PCA and enhanced color properties were used to generate the intermediate images; second, the salient features of an image were extracted by Laplacian of Gaussian; third, the spatial frequency was used as the focus measurement to obtain the final fused image. In 2020, Tyagi et al [26] proposed a hybrid and parallel processing fusion technique for multi-focus images based on stationary wavelet transform (SWT) and principal component analysis (PCA). Recently, more and more researchers have carried out research on multi-focus image fusion methods based on deep learning.…”

Section: Multi-focus Image Fusionmentioning

confidence: 99%

Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis

et al. 2020

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Multi-sensor image fusion is used to combine the complementary information of source images from the multiple sensors. Recently, conventional image fusion schemes based on signal processing techniques have been studied extensively, and machine learning-based techniques have been introduced into image fusion because of the prominent advantages. In this work, a new multi-sensor image fusion method based on the support vector machine and principal component analysis is proposed. First, the key features of the source images are extracted by combining the sliding window technique and five effective evaluation indicators. Second, a trained support vector machine model is used to extract the focus region and the non-focus region of the source images according to the extracted image features, the fusion decision is therefore obtained for each source image. Then, the consistency verification operation is used to absorb a single singular point in the decisions of the trained classifier. Finally, a novel method based on principal component analysis and the multi-scale sliding window is proposed to handle the disputed areas in the fusion decision pair. Experiments are performed to verify the performance of the new combined method.

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A Hybrid Multi-focus Image Fusion Technique using SWT and PCA

Cited by 20 publications

References 15 publications

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation

Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis

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