Abstract
Random-valued impulse noise removal from images is a challenging task in the field of image processing and computer vision. In this paper, an effective three-step noise removal method was proposed using local statistics of grayscale images. Unlike most existing denoising algorithms that assume the noise density is known, our method estimated the noise density in the first step. Based on the estimated noise density, a noise detector was implemented to detect corrupted pixels in the second step. Finally, a modified weighted mean filter was utilized to restore the detected noisy pixels while leaving the noise-free pixels unchanged. The noise removal performance of our method was compared with 10 well-known denoising algorithms. Experimental results demonstrated that our proposed method outperformed other denoising algorithms in terms of noise detection and image restoration in the vast majority of the cases.
Highlights
In an 8-bit/pixel image, noisy pixels in images corrupted by SAP can take on either the minimum or maximum intensity (i.e., 0 or 255), while for contaminated images by randomvalued impulse noise (RVIN), corrupted pixels can take any values between 0 and 255
Our method is compared with 10 well-known RVIN removal methods all of which are discussed in the introduction
The run time of our method is longer than some other methods, it should be noted that it achieved better noise detection and image restoration results in the vast majority of the cases
Summary
Image noise is an inevitable consequence of some intrinsic (e.g., sensor) and/or extrinsic (e.g., environment) factors such as imperfections in capturing instruments, bit errors in analogto-digital conversations, malfunctions in camera sensors, and interference in transmission channels. The existence of noise degrades the visual quality of images and adversely affects the performance of image processing and computer vision tasks, like classification, detection, and segmentation. In an 8-bit/pixel image, noisy pixels in images corrupted by SAP can take on either the minimum or maximum intensity (i.e., 0 or 255), while for contaminated images by RVIN, corrupted pixels can take any values between 0 and 255. Detecting noisy pixels contaminated by RVIN is a challenging task. Another challenging issue in detecting the noisy pixels is distinguishing between image edge pixels and corrupted pixels. The big difference between the intensity of image edge and their neighboring pixels might cause noise detectors to falsely detect the image edge pixels as noisy pixels
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