Feature Extraction of Image using Progressively Enhanced Convolutional Neural Networks

Abstract

processing and analysis tasks. For single-image super resolution (SR) processing, previous CNN-based methods have led to significant improvements, when compared to the shallow learning-based methods. However, these CNN-based algorithms with simply direct or skip connections are not suitable for imagery SR because of complex imaging conditions and unknown degradation process. More importantly, they ignore the extraction and utilization of the structural information in images, which is very unfavorable for video satellite imagery SR with such characteristics as small ground targets, weak textures, and over-compression distortion. To this end, this letter proposes a novel progressively enhanced network for s image SR called PECNN, which is composed of a retraining CNN based network and an enhanced dense connection network. The retraining part is used to extract the low-level feature maps and reconstructs a basic high-resolution image from the low-resolution input. In particular, we propose a transition unit to obtain the structural information from the base output. Then, the obtained structural information and the extracted low-level feature maps are transmitted to the enhanced network for further extraction to enforce the feature expression.