SF-CNN: A Fast Compression Artifacts Removal via Spatial-To-Frequency Convolutional Neural Networks
Taeoh Kim, Hyeongmin Lee, Hanbin Son, Sangyoun Lee · 2019
In this paper, we propose SF-CNN, a fast convolutional neural network structure for JPEG image compression artifacts removal. Recently, Convolutional Neural Network (CNN)-based image restoration has shown great performance improvement. However, its heavy computational cost makes it difficult to apply to other uses such as high-level vision tasks. Since heavy computation arises from maintaining the spatial resolution of an input image, some works make a structure that is composed of spatial downsampling and upsampling operations. SF-CNN takes Spatial input and predicts residual Frequency using downsampling operations only. Since every 8×8 pixel is grouped and spatially invariant in the JPEG DCT domain, it is possible to down sample the input by a factor of 8 to reduce the computational cost. We show this simple structure is effective for compression artifacts removal. Our scalable baseline networks achieve results comparable to to the reference networks in reduced computations.