Abstract
In this paper, a method for the removal of noisy lines and cracks corrupted by different noise types is explored, using a cascade of filtering cycles based on the principle of symmetry among neighboring pixels. Each filtering cycle includes a filter in two perpendicular directions, one horizontal and the other vertical. Any pixel, to be deemed original, should have a number of symmetric pixels within its neighboring pixels greater than the number specified by the condition set for each direction in all the filters. Since the conditions of each filter increase gradually from one cycle to the next, it becomes more difficult for a noisy pixel to satisfy the filter conditions in each filtering cycle, while an original pixel can easily satisfy the conditions in all the filtering cycles. The reason is that a noisy pixel has a random value and therefore faces difficulty in finding a sufficient number of symmetric pixels in each direction, while an original one has a value correlated with the values of its neighboring pixels. Extensive simulation experiments prove that the proposed method efficiently detects and restores different noisy lines and cracks of different shape and thickness. Also, it retains the image details and outperforms other well-known algorithms, both objectively and subjectively. More specifically, the proposed algorithm achieves restoration performance better than the other known methods by ≥0.81dB in all simulation experiments.
Highlights
Image denoising is one of the important processes that should be implemented before any advanced image processing method
Different simulation experiments are implemented on different image structures to show the strength of the proposed method in removing the noisy lines and cracks in images
Noisy pixels in the lines or cracks may pass through one or more filtering cycles depending on the thickness of the lines or cracks, but they face difficulties in passing through all the filtering cycles of the proposed algorithm
Summary
Image denoising is one of the important processes that should be implemented before any advanced image processing method. Various approaches are implemented for Gaussian noise removal, such as the bi-linear filter, [1] which utilizes internal prior information obtained due to the similarity attributes among the pixels in the local region to restore a corrupted pixel. Many others are applied for Gaussian noise removal that utilize internal prior information available among the pixels in non-local regions, such as the NLM algorithm [2,3]. The authors in [12] map external prior information onto the corrupted image to generate a restored image. The advantage of this method is that it can be applied on a few training samples
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