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Aerial and Satellite Image Denoising using Least Square Weighted Regularization Method
Remotely sensed images are subjected to various types of noises. Noise interrupts the image information; hence noise removal is one of the important pre-processing steps in every image processing applications. Since both noise and edges contain highintensity values, image denoising leads to smoothening of the edges thereby reducing the visual quality of the image. Hence, edge preserved image denoising is an ever-relevant topic. Over decades, several image denoising techniques were developed. Most of the denoising algorithms are very complex and time consuming. Background/Objectives: This paper introduces a novel image denoising technique based on least square weighted regularization. Methods/Statistical Analysis: The onedimensional signal denoising introduced by14 is mapped into two-dimensional image denoising. The proposed method is experimented on a set of colored aerial and satellite images. The column-wise denoising of the image is performed first, followed by row-wise denoising. The performance of the proposed method is evaluated based on the standard quality metric peak signal-to-noise ratio and computational time. Findings: From the experimental results, it is observed that the proposed method outperforms the earlier denoising methods on the basis of time and complexity. Applications/Improvements: The proposed denoising technique can be adopted as a faster pre-processing step in most of the image processing applications.
Least Square, Legendre-Fenchel, Peak Signal-to-Noise Ratio, Total Variation, Wavelet.
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