With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving
research area. However, despite promising results, the field still faces challenges that …

The tradeoff between receptive field size and efficiency is a crucial issue in low level vision.
Plain convolutional networks (CNNs) generally enlarge the receptive field at the expense of …

Recent years have witnessed the unprecedented success of deep convolutional neural
networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based …

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem.
However, CNNs depend on deeper network architectures to improve performance of image …

Convolutional neural networks (CNNs) have obtained remarkable performance via deep
architectures. However, these CNNs often achieve poor robustness for image super …

Image super-resolution (SR) techniques with deep convolutional network (CNN) have
achieved significant improvements compared to previous shallow-learning-based methods …

Deep convolutional neural networks (CNNs) have been popularly adopted in image super-
resolution (SR). However, deep CNNs for SR often suffer from the instability of training …

Deep convolutional neural networks (CNNs) with strong expressive ability have achieved
impressive performances on single image super-resolution (SISR). However, their excessive …

Deep convolutional neural networks (CNNs) have been widely applied for low-level vision
over the past five years. According to the nature of different applications, designing …

Light field (LF) photography is an emerging paradigm for capturing more immersive
representations of the real world. However, arising from the inherent tradeoff between the …