Image Super-Resolution via Simplified Dense Network With Non-Degenerate Layers

Abstract

In this paper, we present an efficient method for single image super-resolution based on the dense network. First, considering that the dense architectures reuse all the previous features in each layer and cause a huge memory overhead, we simplify the dense architecture and only reuse the hierarchical features in the reconstruct layer; therefore, the redundancy in dense architecture is reduced. Subsequently, since the information of the degraded low-resolution image is much less than the potential high-resolution image, we propose a non-degenerate layer to address the degeneracy problem caused by the loss of input information. In the non-degenerate layer, we introduce a skip connection to the linear transformation to eliminate the singularity and utilize an invertible nonlinear activation function to avoid the dead zone. In addition, the direct reconstruction scheme is adopted for efficient models, and the structural similarity is utilized for better human perception. The experiments show promising results in terms of quantitative and qualitative results, which indicates that our method is effective and superior.

Keywords

• Convolutional neural network
• deep learning
• image restoration
• image super-resolution