Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

The training of a feature extraction network typically requires abundant manually annotated
training samples, making this a time-consuming and costly process. Accordingly, we …

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 …

Small object detection remains an unsolved challenge because it is hard to extract the
information of small objects with only a few pixels. While scale-level corresponding detection …

Deep convolutional neural networks (DCNNs) have exhibited excellent feature extraction
and detail reconstruction capabilities for single image super-resolution (SISR) …

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

In recent years, with the rapid development of deep learning, super-resolution methods
based on convolutional neural networks (CNNs) have made great progress. However, the …

Deep convolutional neural networks (CNNs) for image denoising have recently attracted
increasing research interest. However, plain networks cannot recover fine details for a …

Convolutional neural networks based single-image superresolution (SISR) has made great
progress in recent years. However, it is difficult to apply these methods to real-world …

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