DEEP CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE SUPER-RESOLUTION

Recently, image super-resolution methods have attained impressive performance by using deep convolutional neural networks (DCNNs). In this work, we describe the algorithmic advances and results obtained by the proposed methods in the image Super-Resolution field. First, we propose a series of Single Image Super-Resolution (SISR) methods for effective and efficient super-resolution tasks. We initially propose a deep feed-forward CNNs method that follows the realistic degradation model by handling the blur kernels of different sizes and different noise levels in an unified network. Next, we propose a deep iterative residual CNNs method that follows the image observation (physical) model by exploiting the powerful image regularization and large-scale optimization techniques with the residual learning. Then, we proposed an efficient deep iterative CNNs method that solves the SR task by cascading the deep residual denoiser networks. Second, we propose SR approaches for the Real-World super-resolution problem. For this purpose, we first propose a deep residual GAN-based SR approach with an adversarial training the network for the pixel-wise supervision of the generated realistic LR/HR pairs. Next, we propose a deep cyclic GAN-based SR method by translating the LR to HR domain and vice versa in an end-to-end manner. After that, we incorporate the learnable adaptive sinusoidal non-linearities into the LR and SR network, whose parameters are optimized during the network training. Finally, we explore the multi image super-resolution (MISR) problems. We first propose a deep star GAN-based SR method by training the network with a single model to super-resolve the LR images for the multiple LR degradation domains. Next, we propose a deep iterative burst SR method that adopts the burst photography pipeline by following the image observation (physical) model. Lastly, we discuss the broader impact, limitations of the current research work, and possible future research dimensions in the image super-resolution field.

Recently, image super-resolution methods have attained impressive performance by using deep convolutional neural networks (DCNNs). In this work, we describe the algorithmic advances and results obtained by the proposed methods in the image Super-Resolution field. First, we propose a series of Single Image Super-Resolution (SISR) methods for effective and efficient super-resolution tasks. We initially propose a deep feed-forward CNNs method that follows the realistic degradation model by handling the blur kernels of different sizes and different noise levels in an unified network. Next, we propose a deep iterative residual CNNs method that follows the image observation (physical) model by exploiting the powerful image regularization and large-scale optimization techniques with the residual learning. Then, we proposed an efficient deep iterative CNNs method that solves the SR task by cascading the deep residual denoiser networks. Second, we propose SR approaches for the Real-World super-resolution problem. For this purpose, we first propose a deep residual GAN-based SR approach with an adversarial training the network for the pixel-wise supervision of the generated realistic LR/HR pairs. Next, we propose a deep cyclic GAN-based SR method by translating the LR to HR domain and vice versa in an end-to-end manner. After that, we incorporate the learnable adaptive sinusoidal non-linearities into the LR and SR network, whose parameters are optimized during the network training. Finally, we explore the multi image super-resolution (MISR) problems. We first propose a deep star GAN-based SR method by training the network with a single model to super-resolve the LR images for the multiple LR degradation domains. Next, we propose a deep iterative burst SR method that adopts the burst photography pipeline by following the image observation (physical) model. Lastly, we discuss the broader impact, limitations of the current research work, and possible future research dimensions in the image super-resolution field.