N-RPN: Hard Example Learning For Region Proposal Networks


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 downsample 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.

In IEEE International Conference on Image Processing.