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.