Abstract: In industrial computerized tomography (CT), using noisy projection data for reconstruction increases the noise in the reconstructed image and reduces the signal-to-noise ratio (SNR). When the quality of projection data is poor, classical denoising and reconstruction algorithms are ineffective in removing the noise. To improve the quality of low signal-to-noise CT reconstructed images, this study proposes a deep learning-based denoising method. The method integrates squeeze-and-excitation blocks into the decoder phase and adaptively adjusts the weights of the channels to better preserve structural details during the denoising process. Experimental results demonstrate that the proposed method significantly reduces the noise and effectively preserves edge details, outperforming other comparative methods in both visual quality and quantitative values.