Image Resolution and Contrast Enhancement of Satellite Geographical Images with Removal of Noise using Wavelet Transforms

TL;DR

This paper proposes a novel method combining wavelet transforms, SVD, and histogram equalization to enhance resolution and contrast of satellite images while effectively removing noise, outperforming traditional techniques.

Contribution

It introduces an integrated approach using DWT, SWT, and SVD for simultaneous resolution and contrast enhancement of satellite images with noise removal.

Findings

Proposed method outperforms conventional techniques in image quality.

Effective noise removal using median filter.

Enhanced resolution and contrast demonstrated through experiments.

Abstract

In this paper the technique for resolution and contrast enhancement of satellite geographical images based on discrete wavelet transform (DWT), stationary wavelet transform (SWT) and singular value decomposition (SVD) has been proposed. In this, the noise is added in the input low resolution and low contrast image. The median filter is used remove noise from the input image. This low resolution, low contrast image without noise is decomposed into four sub-bands by using DWT and SWT. The resolution enhancement technique is based on the interpolation of high frequency components obtained by DWT and input image. SWT is used to enhance input image. DWT is used to decompose an image into four frequency sub bands and these four sub-bands are interpolated using bicubic interpolation technique. All these sub-bands are reconstructed as high resolution image by using inverse DWT (IDWT). To increase the contrast the proposed technique uses DWT and SVD. GHE is used to equalize an image. The equalized image is decomposed into four sub-bands using DWT and new LL sub-band is reconstructed using SVD. All sub-bands are reconstructed using IDWT to generate high resolution and contrast image over conventional techniques. The experimental result shows superiority of the proposed technique over conventional techniques. Key words: Discrete wavelet transform (DWT), General histogram equalization (GHE), Median filter, Singular value decomposition (SVD), Stationary wavelet transform (SWT).