To address the issues of excessive noise and blurred edges in X-ray images of aero-engine rotor blades, this paper proposes a noise self-attention （NSA） dual-decoder image denoising network, which effectively enhances image quality. The network introduces a noise self-attention mechanism between the encoder and decoder to strengthen the perception of image features under noise interference. A dual-decoder structure is adopted to perform denoising and edge preservation separately, and a learnable gated fusion mechanism integrates the outputs of the two decoders, enabling the final result to simultaneously suppress noise and preserve blade edge structures. Experimental results demonstrate that the proposed method outperforms comparative algorithms in visual effects for denoising rotor blade X-ray images, achieving a peak signal-to-noise ratio (PSNR) of 33.26 dB and a structural similarity index (SSIM) of 0.883 7, proving the effectiveness of the proposed approach and providing a reliable X-ray image denoising solution for aero-engine blade inspection.