An investigation of deep learning for image processing applications - Hou, Xianxu (2018)
Significant strides have been made in computer vision over the past few years due to the recent development in deep learning, especially deep convolutional neural networks (CNNs). Based on the advances in GPU computing, innovative model architectures and large-scale dataset, CNNs have become the workhorse behind the state of the art performance for most computer vision tasks. For instance, the most advanced deep CNNs are able to achieve and even surpass human-level performance in image classification tasks. Deep CNNs have demonstrated the ability to learn very powerful image features or representations in a supervised manner. However, in spite of the impressive performance, it is still very difficult to interpret and understand the learned deep features when compared to traditional human-crafted ones. It is not very clear what has been learned in the deep features and how to apply them to other tasks like traditional image processing problems.