Compared with traditional methods, current deep learning-based image completion methods have achieved better repair results. However, most of them overlook the establishment of pixel long-distance dependence, and deep learning models have poor performance in dealing with large irregular missing areas, resulting in insufficient overall fit of the generated image. On the other hand, many completion algorithms that retain more detailed information by fusing multi-scale receptive fields are affected by changes in the input scale and the completion target scale as they cannot adjust the receptive field dynamically, resulting in significant artifact errors in the generated results. In response to such problems, this study proposes a completion algorithm based on fast Fourier transform and selective convolutional kernel network, which ensures the efficient operation of the model while achieving pixel long-distance dependence. In addition, this algorithm also improves the selective convolutional kernel network, which can adaptively adjust the corresponding weights according to the contribution of each convolutional kernel feature. It provides accurate local information supplementation for the model, ultimately generating completion results with higher global fusion and richer local details. The experiments on the Celeb-A and Place2 datasets show that the proposed method not only surpasses existing cutting-edge image completion methods in PSNR and SSIM metrics but also has significant advantages in processing images with occlusion rates of over 80%, which can generate more realistic results.