With the emergence of 200 mega pixel QxQ Bayer pattern image sensors, the remosaic technology that rearranges color filter arrays (CFAs) into Bayer patterns has become increasingly important. However, the limitations of the remosaic algorithm in the sensor often result in artifacts that degrade the details and textures of the images. In this paper, we propose a deep learning-based artifact correction method to enhance image quality within a mobile environment while minimizing shutter lag. We generated a dataset for training by utilizing a high-performance remosaic algorithm and trained a lightweight U-Net based network. The proposed network effectively removes these artifacts, thereby improving the overall image quality. Additionally, it only takes about 15 ms to process a 4000x3000 image on a Galaxy S22 Ultra, making it suitable for real-time applications.