The on-line viewing system of the distributed gigapixel image

Mu, Nan; Chen, Hongzhao; Wang, Guijin; Feng, Houyong; Feng, Chen

doi:10.1109/icist.2016.7483398

Cited by 2 publications

(1 citation statement)

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“…The second one is about processing gigantic images. Here gigantic means either based on the dimension of a single image where a single image may of size in Gig pixels [4] or based on the size where a single image may occupy Giga Bytes of memory in computer. Mainly the image processing tools and programs run on a single machine.…”

Section: Introductionmentioning

confidence: 99%

A Methodical Block based Feature Level Image Fusion Technique with Wavelet Transform using Neural Network for Satellite Images

2019

ijeat

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This paper describe about the feature extraction or detection machine learning application which one is wavelet transform integrated with neural network. It has obtained an effective block based feature level with wavelet transform using neural network (BFWN) model for image fusion. In the projected BFWN model, the discrete wavelet transform (DWT) and neural network (NN) are considered for fusing IRS-1D images using LISS- III scanner about the location different areas in India. Also Quick Bird image data and Landsat 7 image data are used to carry out on the proposed BFWN method. The characteristics like contrast visibility, energy of gradient, spatial frequency, variance and edge information are under study. A Feed forward back propagation neural network is trained and tested for categorization since the learning capability of neural network makes it feasible to customize the image fusion process. The trained neural network is used to fuse the two source images. The proposed BFWN model is distinguish, with DWT alone to assess the quality of the fused image. The results obviously show that the proposed BFWN model is a capable and feasible algorithm for image fusion

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Section: Introductionmentioning

confidence: 99%