Hardwood Grain Image Restoration and Enhancement via Gaussian Histogram Specification and Adaptive Color Adjustment

Abstract

Hardwood is widely used in the surface decoration of furniture and wood products due to its rich texture and durable surface, and the improvement of wood grain images is vital to promote the aesthetics of wood surfaces. In order to restore the Gaussian distribution of distorted wood grain images and reproduce a sharp and clear wood surface, a Gaussian histogram specification algorithm based on the constant mean and variance values of red (R), green (G), and blue (B), and an adaptive color adjustment algorithm based on the color extension of R, G, and B histograms was proposed, respectively. Objective evaluation methods of histogram distribution, colorfulness index, contrast index, and sharpness index were used independently to evaluate the visual effect of the images processed by the two algorithms. Objective and subjective evaluation results showed that although the Gaussian method had only a small influence on the visual effect of hardwood grain images, it could restore the distorted images by repairing the irregular color points to weaken the adverse impact on visual impression. Meanwhile, extra attention should be paid to the processing of images with prominent uneven color transitions, because the Gaussian method might have an imperceptible smoothing or enhancing effect. The adaptive color adjustment method had a favorable enhancement effect on most hardwood grain images. However, the color extension coefficients of the over-enhanced images should be reduced to eliminate overcompensation and color shift. Compared with the traditional enhancement method unsharp mask (USM) and the methods designed for sand-degraded images and underwater images, the proposed adaptive color adjustment at the 1.5 coefficient could effectively enhance the images from the perspective of wood grain visibility and color retention.