A Denoising Algorithm for Wear Debris Images Based on Discrete Wavelet Multi-Band Sparse Representation

Abstract

Wear debris image processing techniques are increasingly employed in health monitoring and fault diagnosis for mechanical equipment. However, during the acquisition and storage of wear debris images, substantial noise is often introduced, leading to significant errors in the subsequent feature extraction and health status assessment. Moreover, the denoising process frequently encounters algorithmic shortcomings, resulting in blurred boundary information, noise artifacts, and incomplete boundaries, greatly hindering further research on wear debris images. Thus, this paper proposes the wavelet-K-singular value decomposition-edge (W-KSVD-EDGE) algorithm, which initially performs two-dimensional discrete wavelet decomposition on noisy images through a multi-band decomposition module, dividing them into four sub-bands of high and low frequencies to enhance image denoising performance. Simultaneously, a second-order decomposition is performed, followed by reconstruction, which reduces high-frequency noise and lowers the complexity of subsequent image processing operations. Subsequently, the K-singular value decomposition (KSVD) algorithm is applied to denoise each sub-band of the initially reconstructed images. Applying the KSVD algorithm to images of different frequency bands, its complexity is mitigated, and the efficiency of image denoising is increased. Consequently, the boundaries of the reconstructed images are further optimized using an improved Canny algorithm for edge enhancement, incorporating edge detection coefficients, resulting in better boundary information restoration for wear debris images. Finally, by analyzing the wear debris information of oil samples collected by a ferrograph, the W-KSVD-EDGE algorithm is employed for denoising both ordinary and wear debris images. The results are evaluated using both subjective and objective methods. The effectiveness and versatility of the proposed algorithm are thereby demonstrated.

Keywords

• wear debris images
• edge enhancement
• multi-band
• sparse algorithm