VLSI Implementation of Image Fusion Using DWT- PCA Algorithm with Maximum Selection Rule

Borra, Surya Prasada Rao; Panakala, Rajesh K.; Kumar, Preetam

doi:10.22266/ijies2019.1031.01

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…More layers of CNN simply mean more exceptional training and a more comprehensive calculation of the image's permutations of pixel densities and related characteristics. Images are pre-processed before being input into the convolution network to improve image quality and to locateretrieve the Region-of-Interest [35], [36]. The proposed architecture shown in figure (2) (3) presents a CNN and Hybrid PCA-Guided Filter Image Fusion approach applied to multimodal and multi-exposed medical images.…”

Section: Proposed Convolutional-neural-network and The Hybrid Pca-gui...mentioning

confidence: 99%

Improved Image Fusion Technique Using Convolutional Neural Networks and The Hybrid PCA-Guided Filter

Jagtap¹,

Thepade²

2023

IJCDS

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In order to better accurately forecast the diagnostic features, medical image fusion attempts to combine multi-focus and multimodal medical data into an original image. The outcomes of deep learning-based image processing can be visually beautiful. The incomprehensibility of the outcomes in Medical Imaging is a critical topic. This paper offers a feature-level multi-focus, multi-exposure, and multimodal image fusion using a hybrid layer of Principal Component Analysis (PCA) and Guided Filter (GF) to maximize the anatomical details and eliminate significant noise and artefacts. The proposed method utilizes a Convolutional Neural Network (CNN) based network for feature extraction. The original image is initially decomposed using Principal Component Analysis (PCA). A PCA decreases its dimensionality while preserving all of the essential information in the picture in the first stage and produces a revised weight map. A Guided Filter is used at the PCA output to uphold the edges and further augment the features, reducing the ringing and blurring effects. In the third step, a pre-trained CNN network creates a new weight map by extracting critical characteristics from pictures in the input dataset. The output feature map combines the weight maps produced by the GF and CNN, which is further fused with the reference picture to create the fused image output. The contribution of developed method:• To improvise image quality features by removing noise, ringing and blurring.• Increase the quality of the image by using the hybrid mechanism for extracting more underlying critical features of images [1]. The estimation is based on three multimodal imaging datasets, including CT-MRI, MRI-PET, and MRI-SPECT. Furthermore, the proposed method excels in existing state-of-the-art techniques in terms of fusion quality. .

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Section: Proposed Convolutional-neural-network and The Hybrid Pca-gui...mentioning

confidence: 99%

Improved Image Fusion Technique Using Convolutional Neural Networks and The Hybrid PCA-Guided Filter

Jagtap¹,

Thepade²

2023

IJCDS

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An Area Efficient VLSI Architecture for 1-D and 2-D Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT)

Jana

Tripathi

Chowdhury

et al. 2021

2021 Devices for Integrated Circuit (DevIC)

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Implementation of Realtime Image Fusion for Biomedical Applications Using ICA And Discrete Wavelet Transform

Ezilarasan,

Vishnupriya,

Arumugam

et al. 2024

IJEER

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Image fusion is an extensively used technique in various areas like computer visualization, enhanced diagnostic imaging, radio therapy, automatic object recognition, image analysis, and remote sensing. The main aim of image fusion is to combine several input images into one image containing more information than the individual images. This type of image fusion results in a new image that is easier for computers and humans to see, making it possible for additional image processing operations like object detection, segmentation, and feature extraction. This paper examines the potential application of customized wavelet transform for image fusion. This paper is implemented for biomedical applications by using wavelet-based pixel-level image fusion technique and Independent Component Analysis (ICA) for de-noising. For realization of this, Initially, MRI and CT pictures are extracted using the 2D discrete wavelet transform and then ICA is used to obtain ICA bases and fused on the sub band pictures. The suggested methodology is implemented in FPGA Spartan and Virtex devices since FPGA provides a good platform for image processing in real-time and finally the performance has been compared with the existing methodologies.

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VLSI Implementation of Image Fusion Using DWT- PCA Algorithm with Maximum Selection Rule

Cited by 3 publications

References 16 publications

Improved Image Fusion Technique Using Convolutional Neural Networks and The Hybrid PCA-Guided Filter

Improved Image Fusion Technique Using Convolutional Neural Networks and The Hybrid PCA-Guided Filter

An Area Efficient VLSI Architecture for 1-D and 2-D Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT)

Implementation of Realtime Image Fusion for Biomedical Applications Using ICA And Discrete Wavelet Transform

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