Self-Quotient Image based CNN: A Basic Image Processing assisting Convolutional Neural Network

Abstract

The Convolutional Neural Networks (CNNs) are able to learn basic and high level features hierarchically with the highlight that it implements an end-to-end learning method. However, lacking in the ability to utilize prior information and domain knowledge has led to the neural networks hard to train. In this paper, a method using prior information is proposed, which is by appending prior feature-maps through a bypass input structure. As an implementation, we evaluate a convolutional neural network integrating with the Self-Quotient Image (SQI) algorithm. Through the bypass, we import the feature-maps from the SQI algorithm and concat them with the output of the first convolution layer. With the help of traditional image processing methods, CNNs can directly improve the accuracy and training stability, while the bypass is exactly a consistent point. Finally, the necessity of this bypass pattern is that it avoids the direct modification of original images. As CNNs are able to focus on far richer features than basic image processing methods, it is advisable for us to expose CNNs to the original data. It is exactly the main design idea that we make the output from synergistic processing algorithm bypass from the side.