alexa Resolution Enhancement of the Satellite Image Processi
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
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Research Article

Resolution Enhancement of the Satellite Image Processing using DT CWT Techniques

V Krishnanaik 1, k.Purushotham2
  1. PhD Research Scholar, Dept of ECE, Mewar University, Chittorgarh, Rajashtan, India
  2. Senior Lecturer, Department of Electrical & Computer Engg, Jigjiga University, Jigjiga, Ethiopia
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Abstract

An image is defined as an array, or a matrix, of square pixels (elements of picture) arranged in rows and columns. Image processing is a procedure of converting an image into digital form and carry out some operation on it, in order to get an improved image and take out several helpful information from it. Actually Signal or image processing enhances certain features of the data while suppressing others. For instance, in analysing a fingerprint image against a textured background it may be important to enhance the fingerprint to identify its owner. The appropriate processing would need to focus on features such as the overall texture pattern to be suppressed and the fingerprint's parallel, smoothly curving lines to be enhanced. Typical Image & Signal Processing Applications are used for Computer vision, Face detection ,Feature detection, extraction, and analysis ,Medical image processing, Remote sensing, Speech recognition, Speech synthesis, Speech compression, Audio, noise suppression, Automated map analysis. In this paper Resolution enhancement (RE) schemes (which are not based on wavelets) suffer from the drawback of losing highfrequency contents (which results in blurring). The discrete- wavelet-transform-based (DWT) RE scheme generates artifacts (due to a DWT shift-variant property). A wavelet-domain approach based on dual-tree complex wavelet transform (DT-CWT) and nonlocal means (NLM) is proposed for RE of the satellite images. A satellite input image is decomposed by DT-CWT (which is nearly shift invariant) to obtain high-frequency subbands. The high-frequency subbands and the low-resolution (LR) input image are interpolated using the Lanczos interpolator. The high- frequency subbands are passed through an NLM filter to cater for the artifacts generated by DT-CWT (despite of its nearly shift invariance). The filtered high-frequency subbands and the LR input image are combined using inverse DT-CWT to obtain a resolution-enhanced image. Objective and subjective analyses reveal superiority of the proposed technique over the conventional and state-of-the-art RE techniques.

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