Inverted Residual Fourier Transformation for Lightweight Single Image Deblurring

Single image deblurring aims to restore a sharp image by removing blurred areas in the single image. Such blurred images are not only visually unpleasant, but cause various problems in many applications like image recognition. In recent years, with the development of deep learning, neural networks are used in many single image deblurring. Especially, encoder-decoder structures are widely used for single image deblurring and successfully restore high quality images. However, FLOPs and the number of parameters tend to increase to restore a high-quality image. Thus, this paper proposes a new lightweight network (IRFTNet) based on UNet, which is a widely used basic network of encoder-decoder structures. Our proposed network has three features to improve performance and lightweight. First, a new backbone called Inverted Residual Fourier Transformation block (IRFTblock) based on inverted residual block is introduced to decrease computational complexity. Second, a new module called Lower Feature Synthesis (LFS) is introduced to efficiently transfer encoder information from lower layers to upper layers. Finally, multiple outputs structure proposed in MIMO-UNet is introduced. These improvements resulted in a 32.98dB in PSNR on the GoPro dataset, despite approximately half FLOPs and the number of parameters of DeepRFT. Further ablation studies show the effectiveness of various components in our proposed model.