Abstract
A two-stage denoising algorithm based on local similarity is proposed to process lowly and moderate corrupted images with random-valued impulse noise in this paper. In the noise detection stage, the pixel to be detected is centered and the local similarity between the pixel and each pixel in its neighborhood is calculated, which can be used as the probability that the pixel is noise. By obtaining the local similarity of each pixel in the image and setting an appropriate threshold, the noise pixels and clean pixels in the damaged image can be detected. In the image restoration stage, an improved bilateral filter based on local similarity and geometric distance is designed. The pixel detected as noise in the first stage is filtered and the new intensity value is the weighted average of all pixel intensities in its neighborhood. A large number of experiments have been conducted on different test images and the results show that compared with the mainstream denoising algorithms, the proposed method can detect and filter out the random-value impulse noise in the image more effectively and faster, while better retaining the edges and other details of the image.
Highlights
T HE digital images are often destroyed by impulse noise due to sensor equipment in the process of image acquisition and transmission
EXPERIMENTAL RESULTS AND DISCUSSION In this chapter, a large amount of experimental data is analyzed to illustrate the performance of the proposed impulse noise detection and noise reduction algorithm, and compare it with several latest algorithms
A good noise detector should have the characteristics of low false detection (FD) and missed detection (MD) rates
Summary
T HE digital images are often destroyed by impulse noise due to sensor equipment in the process of image acquisition and transmission. In order to perform operations such as contour extraction, region segmentation and target recognition on the image later, it is necessary to restore the noise image. The impulse noise detectors and filters based on local statistics have been proposed in the early years. Xiong [9] propose the robust outlyingness ratio (ROR) for measuring how impulse like each pixel is, and combined it with non-local mean (NLM) [10] to eliminate general noise. Garnett [11] proposed a sorted absolute difference (ROAD) statistic and a general triangular filter. Dong [12] proposed a ROAD-based rank logarithmic difference (ROLD). These filters perform well in removing RVIN and retaining edges and details, but their filtering effect is highly dependent on an accurate impulse noise detector
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