Noisy Image Restoration Based on Boundary Resetting BDND and Median Filtering with Smallest Window

In this paper, a restoration approach for noisy image is proposed where a boundary resetting boundary discriminative noise detection (BRBDND) and a median filtering with smallest window (MFSW) are applied. In the proposed image restoration approach, two stages are involved: noise detection and noise replacement. The BRBDND is used to detect noisy pixels in an image. If a pixel is uncorrupted, then keep it intact. Or replace it with an uncorrupted neighborhood pixel through the MFSW. Note that miss detection happens in the BDND presented in [17] when the noise density is high. The miss detection is even worse for cases with unbalanced noisy density where the portions for the salt noise and the pepper noise are different. A boundary resetting scheme is incorporated into the BDND. By this doing, the problem of miss detection described above can be prevented. Note that a larger window used in the median filtering leads to a stronger smoothing effect on the restored image. The reported median filtering approaches, like the modified noise adaptive soft-switching median filter (MNASM) in [17], uses larger windows generally. Thus, a median filtering with smallest window (MFSW) is proposed to improve the visual quality of restored image. Two examples are provided to justify the proposed image restoration approach BRBDND/MFSW where comparisons are made with the BDND/MNASM. The results indicate that the proposed BRBDND is able to deal with the miss detection problem in the BDND. It also shows that the proposed MFSW indeed improves the visual quality of restored image as expected. The simulation results suggest that the proposed restoration approach BRBDND/MFSW generally outperforms the BDND/MNASM both in the PSNR and the visual quality of restored image. Key-Words: noise removal, noise detection, median filtering, BDND, image restoration