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,and HH) using the stationary wavelet transform (SWT). Second, Singular value matrices (SVMs) U_A and V_A 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 sub-bands such as (LL,LH,HL,and HH). 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.
Highlights
In recent years, the demand for visual information is increasing exponentially
The following quality measure criterions were used such as mean square error (MSE), peak-to-signal-noise ratio (PSNR) and entropy
The input image has been decomposed into four frequency components (LL, LH, HL, HH) using stationary wavelet transform (SWT)
Summary
The demand for visual information is increasing exponentially. It has been observed that visual information such as photos, audios, videos, etc. is one of the fundamental resources of receiving data which plays an integral role in people’s lives [1]. Most of the information people receive and work with is visual information, audio and image based. The human brain has the capability of processing visual information efficiently. The computer systems have not the ability to interpret, sense and effectively process the visual information. While discussing the visual information and their importance in today’s life, one cannot ignore the growing demand for the quality images in many applications such as geosciences studies [2], biomedical imagining [3], astronomy [4], geographical information systems [5], surveillance [6], and remote sensing [7]. The usage of quality images is an essential requirement to achieve robust results Many factors such as poor illumination, adverse weather conditions, moving object, poor camera resolutions, etc. These factors can extensively influence the contrast and resolution (dimension) of the images
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