A method combining multi-source input fusion-based underwater image enhancement and GAN-driven grayscale image colorization is proposed to mitigate underwater imaging interference and lighten deep learning models for better generalization and real-time performance. Firstly, spatial domain image enhancement algorithms are utilized to pre-enhance the underwater images, obtaining predictions of red information in different channels. Then, the original images and pre-enhanced images are jointly used as input images, enabling the model to access more information. This model introduces the idea of fusing and reconstructing features from different levels of multiple input images, aiming to preserve as much original information and features as possible during the image enhancement process. Finally, a generator capable of colorizing grayscale images is trained using a large dataset of color images. The quality of the output colorized images is improved by defining an objective function composed of multiple loss functions. Experimental results show that compared to commonly used methods for low-light image enhancement and colorization, this method achieves better objective evaluation results in terms of peak signal-to-noise ratio, structural similarity, scale invariant feature transform, thus verifying its excellent performance.
Pedestrian target tracking based on the UAV platform can be widely used in traffic control, field search, and military reconnaissance. It is an important research task of computer vision and intelligent cruise. Aiming at the limitations of the UAV surveillance system in moving pedestrian target tracking, such as background change, pedestrian deformation, occlusion interference, and lack of real-time performance, the dual Kalman filter is used to improve the traditional TLD tracking algorithm, the proposed method can accelerate the correction of the predicted detection area, reduce the disturbance of the environment background and the target deformation to the pedestrian tracking accuracy, and reduce the detection time by using the adaptive adjustment method of the detection area to offset the time cost caused by double Kalman filtering, to improve the Algorithm’s real-time performance. The test results show that the proposed method has high accuracy, stability, and real-time performance in pedestrian target tracking based on the UAV platform.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.