Satellite image enhancement using wavelet-domain based on singular value decomposition

Abstract

Improving the quality of satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve enhanced performance. Different algorithms have been proposed to enhance the quality of satellite images. However, satellite images enhancement is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to enhance the resolution and contrast of satellite images. To improve the quality of satellite images, in this study, first, the resolution of an image is improved. For resolution enhancement, first, the input image is decomposed into four frequency components (LL;LH;HL; andHH) using the stationary wavelet transform (SWT). Second, Singular value matrices (SVMs) UA and VA which contains high-frequency elements of an input image are obtained using singular value decomposition (SVD). Third, the high-frequency components (LH;HL) of an input image are obtained using discrete wavelet transform (DWT) and corrected by SVMs and SWT. Next, the interpolation factor is added and the high-resolution image is obtained using inverse discrete wavelet transform (IDWT). Second, the contrast of the image is optimized. For the contrast enhancement, the image is decomposed using DWT into subbands such as(LL;LH;HL; andHH). Next, the singular value matrix (SVM) of the LL sub-band is obtained which contains the illumination information. Then, SVM is modified to enhance the contrast. Finally, the image reconstructed using the IDWT. In this paper, the results from the method above are compared with existing approaches. The proposed method achieves high performance and yields more insightful results over conventional technique.