Sub-Pixel Convolutional Neural Network for Image Super-Resolution Reconstruction

Abstract

Image super-resolution (SR) reconstruction technology can improve the quality of low-resolution (LR) images. There are many available deep learning networks different from traditional machine learning algorithms. However, these networks are usually prone to poor performance on complex computation, vanishing gradients, and loss of useful information. In this work, we propose a sub-pixel convolutional neural network (SPCNN) for image SR reconstruction. First, to reduce the strong correlation, the RGB mode was translated into YCbCr mode, and the Y channel data was chosen as the input LR image. Meanwhile, the LR image was chosen as the network input to reduce computation instead of the interpolation reconstructed image as used in the super-resolution convolutional neural network (SRCNN). Then, two convolution layers were built to obtain more features, and four non-linear mapping layers were used to achieve different level features. Furthermore, the residual network was introduced to transfer the feature information from the lower layer to the higher layer to avoid the gradient explosion or vanishing gradient phenomenon. Finally, the sub-pixel convolution layer based on up-sampling was designed to reduce the reconstruction time. Experiments on three different data sets proved that the proposed SPCNN performs superiorly to the Bicubic, sparsity constraint super-resolution (SCSR), anchored neighborhood regression (ANR), and SRCNN methods on reconstruction precision and time consumption.

PSRGAN: Perception-Design-Oriented Image Super Resolution Generative Adversarial Network

“…In order to improve the perceptual quality of SR results, the perceptual-driven approach is proposed. Based on the idea of perceptual similarity [31], Li Feifei et al propose perceptual loss in [32]. Then, textures matching loss [33] and contextual loss [34] are introduced.…”

Learning the Frequency Domain Aliasing for Real-World Super-Resolution

“…Single-image super-resolution (SR) aims to enhance the quality of low-resolution images by reconstructing them into high-resolution counterparts. Learning-based methods [1][2][3][4][5][6][7][8][9][10], such as SRCNN [1], VDSR [2], LapSRN [3], RCAN [4], SRGAN [5], and ESRGAN [6], have made significant advancements in achieving impressive results. Typically, these methods require pairs of high-resolution (HR) and low-resolution (LR) images for training.…”