Abstract
Satellite images are corrupted by noise in its acquisition and transmission. The removal of noise from the image by attenuating the high frequency image components, removes some important details as well. In order to retain the useful information and improve the visual appearance, an effective denoising and resolution enhancement techniques are required. In this research, Hybrid Directional Lifting (HDL) technique is proposed to retain the important details of the image and improve the visual appearance. The Discrete Wavelet Transform (DWT) based interpolation technique is developed for enhancing the resolution of the denoised image. The performance of the proposed techniques are tested by Land Remote-Sensing Satellite (LANDSAT) images, using the quantitative performance measure, Peak Signal to Noise Ratio (PSNR) and computation time to show the significance of the proposed techniques. The PSNR of the HDL technique increases 1.02 dB compared to the standard denoising technique and the DWT based interpolation technique increases 3.94 dB. From the experimental results it reveals that newly developed image denoising and resolution enhancement techniques improve the image visual quality with rich textures.
Highlights
Image denoising is used to remove the additive noise while retaining as much important signal features as possible
The performance of the proposed techniques are tested by Land Remote-Sensing Satellite (LANDSAT) images, using the quantitative performance measure, Peak Signal to Noise Ratio (PSNR) and computation time to show the significance of the proposed techniques
The results are carefully analyzed for both Hybrid Directional Lifting (HDL) technique and Discrete Wavelet Transform (DWT) based resolution enhancement technique
Summary
Image denoising is used to remove the additive noise while retaining as much important signal features as possible. Satellite image resolution enhancement has been an important issue in many image processing applications (Gonzalez and Woods, 2002). Interpolation technique is used to increase the number of pixels in an image (Demirel and Anbarjafari, 2010). It has been used in applications namely facial reconstruction (Demirel and Anbarjafari, 2011) and image resolution enhancement (Li et al, 2007). Image resolution enhancement using wavelet is a new topic and many new algorithms have been proposed (Anbarjafari and Demirel, 2010; Li and Orchard, 2001). This study proposes DWT based interpolation technique which decomposes an image into four subbands namely Low-Low (LL), Low-High (LH), High-Low (HL) and High-High (HH).
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