Image super-resolution (SR) is one of the vital image processing methods that improve the
resolution of an image in the field of computer vision. In the last two decades, significant …

This paper reviews the first challenge on efficient perceptual image enhancement with the
focus on deploying deep learning models on smartphones. The challenge consisted of two …

Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …

While the use of bottom-up local operators in convolutional neural networks (CNNs)
matches well some of the statistics of natural images, it may also prevent such models from …

Deep convolutional neural networks (CNNs) have attracted great attention in the field of
image denoising. However, there are two drawbacks:(1) it is very difficult to train a deeper …

Low-light images typically suffer from two problems. First, they have low visibility (ie, small
pixel values). Second, noise becomes significant and disrupts the image content, due to low …

Frame reconstruction (current or future frames) based on Auto-Encoder (AE) is a popular
method for video anomaly detection. With models trained on the normal data, the …

In this paper, we propose a novel method called Residual Steps Network (RSN). RSN
aggregates features with the same spatial size (Intra-level features) efficiently to obtain …

We introduce a new generator architecture, aimed at fast and efficient high-resolution image-
to-image translation. We design the generator to be an extremely lightweight function of the …

Over the last decade, the number of digital images captured per day has increased
exponentially, due to the accessibility of imaging devices. The visual quality of photographs …