Image Outpainting is a technique where the outside of an image is filled in a continuous manner, taking into account the context of the image. The range of applications is extensive, and numerous approaches using different generative models are researched. In recent years, using Generative Adversarial Networks (GAN) has garnered attention due to considerations of generation quality and computational cost. However, conventional methods using GAN often generate low-quality complemented images that are blurry and have low predictive accuracy due to Spectral Bias. This paper presents a novel Image Outpainting algorithm based on WaveFill using wavelet transform and Neural Neighbor Style Transfer (NNST). WaveFill, which efficiently learned high-frequency bands, is employed to initially complement the damaged region, followed by the utilization of NNST to reconstruct the complemented region. The effectiveness of the proposed method is verified through both objective and subjective experiments, demonstrating its superiority over SinGAN and SinGAN + NNST.