Abstract
In this paper, an improved multi-exposure image fusion method for intelligent transportation systems (ITS) is proposed. Further, a new multi-exposure image dataset for traffic signs, TrafficSign, is presented to verify the method. In the intelligent transportation system, as a type of important road information, traffic signs are fused by this method to obtain a fused image with moderate brightness and intact information. By estimating the degree of retention of different features in the source image, the fusion results have adaptive characteristics similar to that of the source image. Considering the weather factor and environmental noise, the source image is preprocessed by bilateral filtering and dehazing algorithm. Further, this paper uses adaptive optimization to improve the quality of the output image of the fusion model. The qualitative and quantitative experiments on the new dataset show that the multi-exposure image fusion algorithm proposed in this paper is effective and practical in the ITS.
Highlights
College of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China; Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China
Paul et al [9] presented multi-exposure and multi-focus image fusion in the gradient domain (GBM). This method is not suitable for multi-exposure images with too large of a gradient difference, and the fusion speed is slower than other methods, so it is not suitable for the intelligent transportation field that needs to process a large amount of information
I1∗, I2∗, I ∗f, ω1, ω2 are sent into the loss function without the need for grounddegree of information retention in the final obtained source images is represented by ω1 truth
Summary
With the rapid development of digital image technology, more and more digital image technologies will be applied to intelligent transportation systems [1]. As an important part of a smart city, intelligent transportation systems (ITS) are the effective comprehensive application of advanced science and technology in the field of transportation. Traditional fusion methods include three main steps: image transformation, activity level measurement, and fusion rule design [3] It is time-consuming, expensive, and difficult to design the feature extraction and fusion rules. The final generated image can reflect the source image information and has great practicability and effectiveness; We have released a new multi-exposure image dataset, TrafficSign [7], which is aimed at the fusion of traffic signs in the intelligent transportation field, and provides a new option for image fusion benchmark evaluation.
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