Defect detections in industrial radiography images by a multi-scale LMMSE estimation scheme

Abstract The defect detection of welded objects is at the core of nondestructive testing and has found many applications in many industries. Industrial radiography is widely used for inspection of welded objects. In the low contrast radiography, image processing can assist to improve image quality. In this paper, an interlaced over complete wavelet expansion (OWE) and linear minimum mean square error estimation (LMMSE) are implemented to detect defects and improve the contrast of industrial radiography images. In the first stage, OWE is implemented to the radiograph and the wavelet coefficients can be calculated. Then, LMMSE can be applied to calculate the threshold level of each scale and the new wavelet coefficient. Finally, to reconstruct the output image, an inverse OWE transform will be applied. The experimental results show that the reconstructed images have higher contrast than the original radiograph in the defect regions.

[1]  Karen O. Egiazarian,et al.  Image denoising with block-matching and 3D filtering , 2006, Electronic Imaging.

[2]  Qian Chen,et al.  Image denoising by bounded block matching and 3D filtering , 2010, Signal Process..

[3]  Fulei Chu,et al.  Morphological undecimated wavelet decomposition for fault diagnostics of rolling element bearings , 2009 .

[4]  J Anthony Seibert,et al.  DIGITAL RADIOGRAPHY: IMAGE QUALITY AND RADIATION DOSE , 2008, Health physics.

[5]  Mohamed-Jalal Fadili,et al.  The Undecimated Wavelet Decomposition and its Reconstruction , 2007, IEEE Transactions on Image Processing.

[6]  Ming Zhang,et al.  Multiresolution Bilateral Filtering for Image Denoising , 2008, IEEE Transactions on Image Processing.

[7]  Surjya K. Pal,et al.  Weld defect identification in friction stir welding through optimized wavelet transformation of signals and validation through X-ray micro-CT scan , 2018, The International Journal of Advanced Manufacturing Technology.

[8]  B. Sheela Rani,et al.  DWT Based Automated Weld Pool Detection and Defect Characterisation from Weld Radiographs , 2014 .

[9]  R. S. Anand,et al.  A Comparative Study of Different Segmentation Techniques for Detection of Flaws in NDE Weld Images , 2012 .

[10]  Il Kyu Eom,et al.  Image contrast enhancement using entropy scaling in wavelet domain , 2016, Signal Process..

[11]  Fabien Feschet Implementation of a Denoising Algorithm Based on High-Order Singular Value Decomposition of Tensors , 2019, Image Process. Line.

[12]  N. M. Nandhitha,et al.  Computer Aided Radiograph Interpretation Tool for Defect Characterization from Weld Plates , 2019, Russian Journal of Nondestructive Testing.

[13]  Sangwook Lee,et al.  Automated recognition of surface defects using digital color image processing , 2006 .

[14]  Carl-Fredrik Westin,et al.  Noise and Signal Estimation in Magnitude MRI and Rician Distributed Images: A LMMSE Approach , 2008, IEEE Transactions on Image Processing.

[15]  N. Galatsanos,et al.  Multiple-image radiography. , 2003, Physics in medicine and biology.

[16]  Xiangdong Gao,et al.  Detection of weld position and seam tracking based on Kalman filtering of weld pool images , 2005 .

[17]  Lei Zhang,et al.  Multiscale LMMSE-based image denoising with optimal wavelet selection , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[18]  Gabriele Facciolo,et al.  Data Adaptive Dual Domain Denoising: a Method to Boost State of the Art Denoising Algorithms , 2017, Image Process. Line.

[19]  Pierre Moulin,et al.  Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients , 2001, IEEE Trans. Image Process..

[20]  Wang-Q Lim,et al.  Sparse multidimensional representation using shearlets , 2005, SPIE Optics + Photonics.