Zhang, X.; Jiang, X.; Song, Q.; Zhang, P. A Visual Enhancement Network with Feature Fusion for Image Aesthetic Assessment. Electronics2023, 12, 2526.
Zhang, X.; Jiang, X.; Song, Q.; Zhang, P. A Visual Enhancement Network with Feature Fusion for Image Aesthetic Assessment. Electronics 2023, 12, 2526.
Zhang, X.; Jiang, X.; Song, Q.; Zhang, P. A Visual Enhancement Network with Feature Fusion for Image Aesthetic Assessment. Electronics2023, 12, 2526.
Zhang, X.; Jiang, X.; Song, Q.; Zhang, P. A Visual Enhancement Network with Feature Fusion for Image Aesthetic Assessment. Electronics 2023, 12, 2526.
Abstract
Abstract: Image aesthetic assessment (IAA) with neural attention has made significant progress due to its effectiveness in object recognition. Current studies have shown that the features learned by convolutional neural networks (CNN) at different learning stages indicate meaningful information. The shallow feature contains the low-level information of images and the deep feature perceives the image semantics and themes. Inspired by this, we propose a visual enhancement network with feature fusion (FF-VEN). It consists of two sub-modules, the visual enhancement module (VE module) and the shallow and deep feature fusion module (SDFF module). The former uses an adaptive filter in the spatial domain to simulate human eyes according to the region of interest (ROI) extracted by neural feedback. The latter not only takes out the shallow feature and the deep feature by transverse connection, but also uses a feature fusion unit (FFU) to fuse the pooled features together with the aim of information contribution maximization. Experiments on standard AVA dataset and Photo.net dataset show the effectiveness of FF-VEN.
Keywords
deep learning; image aesthetics assessment; image enhancement
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.