基于BM3D的钢板表面图像自适应去噪方法

Adaptive denoising method of steel plate surface image based on BM3D

为了解决传统BM3D算法的距离阈值选取无法自适应去除钢板图像中的噪声以改善图像质量的问题,提出了一种基于噪声估计和阈值函数的自适应BM3D去噪算法(TFBM3D)。先采用网格搜索法得到不同钢板缺陷图像在不同噪声强度下基础估计和最终估计的最佳阈值,再通过对比不同函数的拟合效果,最终确定基础估计的二次曲线阈值函数和最终估计的四次多项式阈值函数,并将噪声估计作为新算法前处理阶段。最后将新BM3D算法、原BM3D算法以及一些其他最新的去噪算法进行比较,试验结果表明,该算法在复原缺陷图像边缘和细节纹理上效果显著,在噪声标准差为30的条件下各缺陷图片去噪效果的PSNR值在33 dB以上、SSIM值在0.85以上,且残余图像中残余的存留细节更少,本文算法优于其他算法。

An adaptive block-matching and 3D-filtering denoising(BM3D)algorithm based on noise estimation and a threshold function is proposed to solve the problem of the distance threshold selection of the traditional BM3D algorithm not being adaptive and to improve the image quality by removing noise in steel plate images.First,the grid search method is used to obtain different plate defect images under different noise-intensity-based estimations and the final estimate for the best threshold value.Subsequently,the different function fitting effects are compared,and the estimated quadratic curve threshold function and the final estimate of four polynomial threshold functions are determined.Moreover,noise estimation is performed for the new algorithm processing phase.Finally,the new BM3D algorithm is compared with the original BM3D algorithm and other latest denoising algorithms.Experiments show that the algorithm has excellent performance in restoring the edge and detail textures of defective images.Under noise with a standard deviation of 30,the peak signal-to-noise ratio and structural similarity value of the denoising effect of each defective image are above 33 dB and 0.85,respectively.Moreover,the residual details in the residual image are reduced and are better than those achieved by applying other algorithms.