Anisotropic diffusion based denoising on X-radiography images to detect weld defects

This paper proposes a machine vision scheme for denoising, feature space gradient preserving, and detecting weld defects in noisy weld X-radiography images; particularly, for the images that are in low-contrast and contain noises. The detection of small weld defects present on noisy image is extremely difficult in non-destructive testing through machine vision. The presence of high gradient magnitude and the low intensity in the feature space of a noisy image are the main characteristics of weld defects. These characteristics can be considered to refine and obtain noise-free images for detection of weld defects. This study proposes a modified anisotropic diffusion model, which considers a local probability value of gray-level and an adaptive threshold parameter in diffusion coefficient function to adjust the implication of low edge gradient of the feature space from the noisy image. Furthermore, an entropy based stopping criterion has been introduced to terminate the diffusion process. This proposed model is compared with the existing models, and its performance is evaluated through Mean Square Error (MSE), Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Entropy (E) and Mean Structural Similarity (MSSIM) measures. Experimental results confirm the reliability of the proposed model.