An Efficient Adaptive Denoising Algorithm for Remote Sensing Images

Abstract

Typically, after the capturing, imaging, and transferring processes have been accomplished, the digital images will contain a variety of noise, caused by both the equipment itself and by the complex working environment. Consequently, it is necessary to perform a de-noising process to facilitate the extraction of useful information. This paper presents a fast and efficient denoising algorithm, which combines the advantages of traditional median filters and weighted filter algorithms. In this algorithm, the noise in the figure is determined, and those results are applied to adaptively change the size of the window, while assigning different weights to the pixels in the filter window. The experimental results show that we can significantly remove almost all salt and pepper noise, while retaining full image textures, edges, and other minutiae.

Removal of Salt-and-Pepper Noise Using a High-Precision Frequency Analysis Approach

“…This is caused mainly by an error in a sensor or communication channel. In the case of a communication error, in particular, an impulse noise may significantly deteriorate a satellite photo image [1]. An impulse noise converts pixel data in the image into black (0) or white (255) values at a random frequency and is called salt-and-pepper noise because of this characteristic.…”

Image Denoising via Asymptotic Nonlocal Filtering

“…Denoising is always an important research in image processing and computer vision. Numerous denoising techniques have been proposed in the image processing literature including variation regularization [1][2][3][4][5][6], Partial Differential Equation [7,8], wavelet shrinkage [9], and neighborhood filtering [10,11]. In particular, the nonlocal means (NLM) algorithm, which is first presented in [12] by Buades et al, has drawn much research attention lately due to its excellent performance.…”