Abstract
It is essential to restore digital images corrupted by noise to make them more useful. Many approaches have been proposed to restore images affected by fixed value impulse noise, but they still do not perform well at high noise density. This paper presents a new method to improve the detection and removal of fixed value impulse noise from digital images. The proposed method consists of two stages. The first stage is the noise detection stage, where the difference values between the pixels and their surrounding pixels are computed to decide whether they are noisy pixels or not. The second stage is the image denoising stage. In this stage, the original intensity value of the noisy pixels is estimated using only their first-order and second-order neighborhood pixels. These neighboring orders are based on the Euclidean distance between the noisy pixel and its neighboring pixels. The proposed method was evaluated by comparing it with some of the recent methods using 50 images at 18 noise densities. The experimental results confirm that the proposed method outperforms the existing filters, excelling in noise removal capability with structure and edge information preservation.
Highlights
Digital images have an important role in our life [1]
Many images that are created have some imperfections called noise [6]. Such imperfections may be caused by the incapacity and instability of imaging systems or sensors to obtain ideal images [1], or natural disruptions in the surroundings during image processing [1,7], inadequate illumination, or sensor temperature resulting in noise, or during compression and transmission [1,7,8]
The proposed method is based on switching median filter (SwMF)
Summary
Digital images have an important role in our life [1]. Digital imaging has a wide range of applications in many research areas, such as medical sciences, biology, particle physics, geology, the science of materials, photography and remote sensing [1,2,3]. Many images that are created have some imperfections called noise [6] Such imperfections may be caused by the incapacity and instability of imaging systems or sensors to obtain ideal images [1], or natural disruptions in the surroundings during image processing [1,7], inadequate illumination, or sensor temperature resulting in noise, or during compression and transmission [1,7,8]. Data obtained with these noises can make the data unusable or lose confidence in them [1,9]
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