A noise reduction algorithm for industrial CT images incorporating wavelet denoising and guided filtering

To address the issues of fuzzy edges and loss of fine features in existing noise reduction algorithms for industrial computed tomography (CT) images, we present a hybrid wavelet denoising and guided filtering algorithm. Firstly, a 2-layer wavelet decomposition is performed on the industrial CT image using the Haar wavelet basis to decompose the original image into low frequency and high frequency coefficients. Secondly, the high frequency coefficients are processed using the threshold shrinkage method, and the low frequency coefficients are refined by combining them with guided filtering. Lastly, the wavelet coefficients are reconstructed using the wavelet inverse transform to obtain the final noise reduced image. Experimental results show that the peak signal to noise ratio (PSNR) of the proposed algorithm reaches 43.8013 dB, which is 34.87%, 17.84%, and 5.01% higher than that of bilateral filtering, wavelet denoising, and guided filtering algorithms, respectively. Both simulation experiments and practical applications demonstrate the effectiveness of the proposed algorithm in preserving edge and detail information, improving the quality of industrial CT images and providing strong support for noise reduction technology based on industrial CT images.