-
AE-Net: Autonomous Evolution Image Fusion Method Inspired by Human Cognitive Mechanism
In order to solve the robustness and generality problems of the image fu...
read it
-
A Cross-Modal Image Fusion Theory Guided by Human Visual Characteristics
The characteristics of feature selection, nonlinear combination and mult...
read it
-
A Robust Non-Linear and Feature-Selection Image Fusion Theory
The human visual perception system has strong robustness in image fusion...
read it
-
Cross-Modal Image Fusion Theory Guided by Subjective Visual Attention
The human visual perception system has very strong robustness and contex...
read it
-
Scalable Panel Fusion Using Distributed Min Cost Flow
Modern audience measurement requires combining observations from dispara...
read it
-
Image deblurring based on lightweight multi-information fusion network
Recently, deep learning based image deblurring has been well developed. ...
read it
-
Unification of Fusion Theories, Rules, Filters, Image Fusion and Target Tracking Methods (UFT)
The author has pledged in various papers, conference or seminar presenta...
read it
AE-Netv2: Optimization of Image Fusion Efficiency and Network Architecture
Existing image fusion methods pay few research attention to image fusion efficiency and network architecture. However, the efficiency and accuracy of image fusion has an important impact in practical applications. To solve this problem, we propose an efficient autonomous evolution image fusion method, dubed by AE-Netv2. Different from other image fusion methods based on deep learning, AE-Netv2 is inspired by human brain cognitive mechanism. Firstly, we discuss the influence of different network architecture on image fusion quality and fusion efficiency, which provides a reference for the design of image fusion architecture. Secondly, we explore the influence of pooling layer on image fusion task and propose an image fusion method with pooling layer. Finally, we explore the commonness and characteristics of different image fusion tasks, which provides a research basis for further research on the continuous learning characteristics of human brain in the field of image fusion. Comprehensive experiments demonstrate the superiority of AE-Netv2 compared with state-of-the-art methods in different fusion tasks at a real time speed of 100+ FPS on GTX 2070. Among all tested methods based on deep learning, AE-Netv2 has the faster speed, the smaller model size and the better robustness.
READ FULL TEXT
Comments
There are no comments yet.