A Review on Deep Learning Super Resolution Techniques

Super resolution techniques are used to reconstruct the detail of high-resolution image from low-resolution lossy image. The introduction of a deep learning approach in super resolution has drawn a lot of research interest in recent years due to its learning capability and noise immunity. The applications of deep learning super resolution can mainly be found in image recovery, medical imaging, and microscopy. In this paper, the deep learning super resolutions are explored in detail based on its models and architecture. They can be classified into three neural network (NN) models, i.e. Convolutional NN-based models, Recursive NN-based models, and Adversarial Network-based models. CNN-based models apply convolution operation to embed the latent feature and subsequently decode it with deconvolution operation to achieve a higher dimension. RNN-based models integrate the recursive depth model to enhance recursive learning with past memory. On the other hand, adversarial network-based models apply the generative manner to learn the probability of the input pattern to forecast the possible high dimension of output information. The details of each unsupervised model are discussed in this paper to highlight its advantages and limitations. The measurement metrics such as Peak Signal-to-Noise Ratio (PSNR), structural similarity (SSIM) and Mean opinion score (MOS) are highlighted for performance evaluation of each super resolution models. The significance of this study provide a compact review of the current development and trend in super resolution using various deep learning models.