Qureshi,H., Malik,M., Ahmad,M.A., Heinzl,C.
Abstract:
De-noising is one of the most important applications of image processing, which has found application in a wide variety of domains. De-noising allows for improving image quality in noise prone imaging modalities. Although, de-noising has been applied rigorously in 2D imaging, but new modalities of imaging such as industrial 3D X-ray computed tomography (3DXCT) have not received much attention. Industrial XCT scanning is used today to acquire images of industrial components and machinery so that defects within these can be identified non-destructively and non-intrusively. However, the main problem in XCT is that XCT imaging is prone to artifacts and noise in the generated data. One solution to the problem is the increased number of projections which results in reduced noise but leads to increased cost. In this paper, we show how various de-noising techniques may be used to de-noise 3DXCT scan images. We also benchmark these techniques using various picture quality measures. Our investigation shows that high quality results may be obtained using wavelet shrinkage and anisotropic diffusion.