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Application of Bayesian Belief Networks for Smart City Fire Risk Assessment Using History Statistics and Sensor Data Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Image Enhancement Method in Decompression Based on F-shift Transformation

Authors : Ruiqin Fan, Xiaoyun Li, Huanyu Zhao, Haolan Zhang, Chaoyi Pang, Junhu Wang

Published in: Data Science

Publisher: Springer Singapore

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Abstract

In order to process a compressed image, such as a JPG image, a common way is to decompress the image first to get each pixel, and then process it. In this paper, we propose a method for image enhancement in the process of decompression. The image is compressed by using two-dimensional F-shift (TDFS) and two dimensional Haar wavelet transform. To enhance the image during decompression, firstly we adjust the brightness of the whole image by modifying the approximation coefficient and enhance the decompressed low frequency component part using the contrast limited adaptive histogram equalization (CLAHE) method. Finally, we decompose the remaining data and do the last step of image enhancement. Contrast with CLAHE and the state-of-art method, our method can not only combination merits of the spatial domain method and the transform domain method, but also can reduce the process complexity and maintain the compressibility of the original image.

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Metadata
Title
Image Enhancement Method in Decompression Based on F-shift Transformation
Authors
Ruiqin Fan
Xiaoyun Li
Huanyu Zhao
Haolan Zhang
Chaoyi Pang
Junhu Wang
Copyright Year
2020
Publisher
Springer Singapore
Book
Data Science

Print ISBN: 978-981-15-2809-5

Electronic ISBN: 978-981-15-2810-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-2810-1_23

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