Research on the veneer defect image enhancement algorithm based on AMEF-AGC.

Abstract

Aiming at the veneer defect image acquisition process is prone to the problems of blurred edges, inconspicuous contrast and distortion, which cannot show the defects clearly.To improve image analyzability and clarity, a veneer defect image enhancement method based on AMEF-AGC is proposed herein. First, a veneer defect image is subjected to Gamma correction to obtain multiple underexposed image sequences for which Gaussian and Laplacian pyramids are constructed to determine the weights of the multiple exposure sequence group images.Multiscale fusion is then performed based on these weights. Second, the fused image is converted into the HSV color space, where contrast and brightness enhancements are performed for the luminance component, and then converted back to the RGB color space to obtain an enhanced veneer defect image. Solid wood panels selected herein were pine, poplar and birch with defects including live knots, dead knots, and cracks.Compared with those obtained using several algorithms including AMEF, AGC, improved AGC and GC, this algorithm achieved 6.93% and 5.4% improvements in PSNR and SSIM metrics, respectively.Results demonstrated that the proposed method effectively enhanced veneer defect images with blurred and distorted edges, improved image clarity and visual quality, and made defect parts and details of the image clearer.