Lightweight Infrared and Visible Image Fusion via Adaptive DenseNet with Knowledge Distillation

Abstract

The fusion of infrared and visible images produces a complementary image that captures both infrared radiation information and visible texture structure details using the respective sensors. However, the current deep-learning-based fusion approaches mainly tend to prioritize visual quality and statistical metrics, leading to an increased model complexity and weight parameter sizes. To address these challenges, we propose a novel dual-light fusion approach using adaptive DenseNet with knowledge distillation to learn and compress from pre-existing fusion models, which achieves the goals of model compression through the use of hyperparameters such as the width and depth of the model network. The effectiveness of our proposed approach is evaluated on a new dataset comprising three public datasets (MSRS, M3FD, and LLVIP), and both qualitative and quantitative experimental results show that the distillated adaptive DenseNet model effectively matches the original fusion models’ performance with smaller model weight parameters and shorter inference times.

Gaussian of Differences: A Simple and Efficient General Image Fusion Method

“…Therefore, one of the primary factors considered in designing the proposed image fusion method was keeping the computational complexity low. Another significant concern was the generation of a single composite image that incorporates meaningful information from images captured at multiple or diverse wavelengths [ 51 ]. The resulting combined image should be suitable for both human interaction and computer vision applications [ 52 ].…”

Lightweight Tunnel Defect Detection Algorithm Based on Knowledge Distillation

“…Zhang [14] et al used a knowledge distillation method for human posture prediction to enable lightweight deployment without altering the model's structure. A similar information distillation technique was applied by Zhao [15] et al to produce a lightweight fusion of infrared and visible pictures. Hinton [16], Romero [17], and Zagoruyko [18] have each put forth a variety of knowledge distillation-based methodologies.…”