Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Visual Enhancement Network with Feature Fusion for Image Aesthetic Assessment

Version 1 : Received: 27 April 2023 / Approved: 28 April 2023 / Online: 28 April 2023 (03:15:16 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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

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