Fast and efficient implementation of image filtering using a side window convolutional neural network

Abstract

Convolutional neural networks (CNNs) designed for object recognition have been successfully applied to low-level tasks such as image filtering. However, these networks are usually very large which occupy large memory space and demand very high computational capacity. This makes them unsuitable for real time low-level applications on smart and portable devices with limited memory and computational capacities. In this paper, we specifically design a novel CNN, side window convolutional neural network (SW-CNN), for the fast and efficient implementation of image filtering. In SW-CNN, a new convolutional strategy, called side kernel convolution (SKC) is proposed which aligns the side or corner of the convolutional window with the pixels under processing to preserve edges during convolution. By combining SKC and the representational power of CNNs, SW-CNN can learn various image-filtering tasks very effectively. Compared to the state-of-the-art networks, the superiority of SW-CNN includes three aspects. First, the number of learnable parameters is reduced by 96%. Second, the memory consumption is reduced to 50%. Third, the running time is decreased to 50%. Results of extensive experiments demonstrate that SW-CNN not only has good performance on implementing various edge-preserving filters, but also has the adaptability and flexibility on other low-level image processing applications.