Drop Size Distribution of Rain Droplet Using Non-Subsampled Contourlet Transform and Double Density Dual tree Transform

DOI

10.2991/iccasp-16.2017.104How to use a DOI?

Keywords

Drop size distribution; Patchy; Non Subsampled Contourlet Transform; Double-density Dual-tree Discrete Wavelet Transform; Fusion.

Abstract

This paper deals with algorithm for extraction information from visualized droplets using signal processing. Study show that rain is "patchy" in nature and consists of patches of Drop Size Distributions (DSD) over differ-ent scales. The characteristics of high resolution, shift-invariance, and high directionality of Non Subsampled Contourlet Transform (NSCT) and Double-density Dual-tree Discrete Wavelet Transform (DDDWT) are test-ed. Both transform representing the directional information and capturing intrinsic geometrical structures of the objects. In this paper two transform tested to fuse images and relative parameters are tested to suggest effec-tive transform. Hence, we propose to exploit algorithm to extract information DSD with detail information of rainfall. The proposed methods are tested Rain Images-2 datasets and compared with the standard parameters. Visual and quantitative results demonstrate the efficiency of the proposed methods.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - P. Ghonge
AU  - K. Tuckley
PY  - 2016/12
DA  - 2016/12
TI  - Drop Size Distribution of Rain Droplet Using Non-Subsampled Contourlet Transform and Double Density Dual tree Transform
BT  - Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)
PB  - Atlantis Press
SP  - 742
EP  - 748
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasp-16.2017.104
DO  - 10.2991/iccasp-16.2017.104
ID  - Ghonge2016/12
ER  -