A Denoising Scheme for Scanned Wood Grain Images via Adaptive Color Substitution

Abstract

Real-world image denoising is a challenging problem in low-level vision. In order to reduce the luminance noise on scanned wood grain images randomly generated by the Microtek Phantom 9900XL scanner, the images were classified and sorted according to the noise size. The proposed denoising scheme reduces noise by substituting dissimilar pixels within a certain window size. The No.1 to No. 6 wood images with noise size of approximately (or no greater than) 3 pixels × 3 pixels were processed using coarse denoising with a 7 × 7 window (α = 100, β = 30), fine denoising with a 5 × 5 window (α = 90, β = 40), and the Dust & Scratches filter at settings of 1 (pixels) and 35 (levels). The No.7 to No. 16 wood images with noise size of approximately (or no greater than) 1 pixel × 1 pixel were processed using fine denoising with a 5 × 5 window (α = 100, β = 30), and the Dust & Scratches filter at settings of 1 (pixel) and 35 (levels). The proposed Scheme I and II was then compared with Wiener filtering, Gaussian filtering, median filtering, and the Dust & Scratches filter under designated settings. The results of subjective and objective evaluations demonstrated that the proposed Scheme outperformed the above denoising methods on reducing the luminance noise. When using the median values of R (red), G (green), and B (blue) channels within a certain window to substitute the R, G, and B values of the luminance noise, the denoising ranges of α≥100 and β≤30 were suitable for the No.1 to No.16 wood images.