Infrared and Visible Image Fusion with Hybrid Image Filtering

Abstract

Image fusion is an important technique aiming to generate a composite image from multiple images of the same scene. Infrared and visible images can provide the same scene information from different aspects, which is useful for target recognition. But the existing fusion methods cannot well preserve the thermal radiation and appearance information simultaneously. Thus, we propose an infrared and visible image fusion method by hybrid image filtering. We represent the fusion problem with a divide and conquer strategy. A Gaussian filter is used to decompose the source images into base layers and detail layers. An improved co-occurrence filter fuses the detail layers for preserving the thermal radiation of the source images. A guided filter fuses the base layers for retaining the background appearance information of the source images. Superposition of the fused base layer and fused detail layer generates the final fusion image. Subjective visual and objective quantitative evaluations comparing with other fusion algorithms demonstrate the better performance of the proposed method.

Use Of Infrared Data For Detection Of Concrete Defect In Service Thrust Block Based On Heat Balance Analysis

“…High accuracy has been confirmed at several attempts, however, a disadvantage is decreased accuracy for unknown structures [2]. The detection of defects by fusion images of infrared and visual has been reported to get reliable a result [3][4]. Especially, surface temperature from infrared images is a sensitive and unique parameter determined by environment and defect conditions.…”

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

“…Based on the different feature extraction and fusion strategies, these methods can be classified into conventional fusion methods and end-to-end deep learning methods. According to the hand-crafted feature decomposition and generation rules, conventional fusion methods mainly consist of multiscale transform-based [10], sparse representation-based [11][12][13], saliency-based [14][15][16][17], fuzzy set-based [18][19][20], and hybridbased [21][22][23] methods. To summarize, conventional methods for image fusion typically comprise three primary stages.…”

Local Feature Filtering Method for Dynamic Multiframe Video Sequence Images

“…With the socioeconomic development, human beings are demanding higher and higher standards for the viewing and working quality of video images [1]. However, video shooting may result in various types of noise in the video due to camera shake, insufficient light, or uneven distribution, which affects the use and viewing of the video by users or consumers [2].…”