Current printed circuit board (PCB) image-denoising algorithms can easily produce excessive edge smoothing and detail loss in the denoising process. To improve the effect of PCB image denoising, this paper proposes a PCB image-denoising algorithm based on residual learning and image difference. First, an image downsampling method is used to expand the receptive field of the image based on the idea of residual learning. Thereafter, a residual block is designed to extract the noise characteristics of the PCB image. Meanwhile, batch normalization and ReLU activation function are added to the residual convolutional neural network element to improve the denoising efficiency. Finally, the noise is removed through the image difference process. The experimental denoising performance of various algorithms is compared under different noise levels and the results show that the algorithm proposed in this paper has better performance than other algorithms in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).