This article reviews recent computer vision techniques used in the assessment of image
aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high …

The emergence of artificial emotional intelligence technology is revolutionizing the fields of
computers and robotics, allowing for a new level of communication and understanding of …

Despite deep end-to-end learning methods have shown their superiority in removing non-
uniform motion blur, there still exist major challenges with the current multi-scale and scale …

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors
including composition, color, style, and high-level semantics. Existing image aesthetic …

Automatically learned quality assessment for images has recently become a hot topic due to
its usefulness in a wide variety of applications, such as evaluating image capture pipelines …

Rain streaks, particularly in heavy rain, not only degrade visibility but also make many
computer vision algorithms fail to function properly. In this paper, we address this visibility …

In this paper, we present an extensive review on deep learning methods for image
restoration tasks. Deep learning techniques, led by convolutional neural networks, have …

We study the problem of photo crop**, which aims to find a crop** window of an input
image to preserve as much as possible its important parts while being aesthetically …

Real-world applications could benefit from the ability to automatically generate a fine-
grained ranking of photo aesthetics. However, previous methods for image aesthetics …

Photo aesthetics assessment is challenging. Deep convolutional neural network (ConvNet)
methods have recently shown promising results for aesthetics assessment. The performance …