High-speed hardware accelerator based on brightness improved by Light-DehazeNet

Abstract

Due to the increasing demand for artificial intelligence technology in today’s society, the entire industrial production system is undergoing a transformative process related to automation, reliability, and robustness, seeking higher productivity and product competitiveness. Additionally, many hardware platforms are unable to deploy complex algorithms due to limited resources. To address these challenges, this paper proposes a computationally efficient lightweight convolutional neural network called Brightness Improved by Light-DehazeNet, which removes the impact of fog and haze to reconstruct clear images. Additionally, we introduce an efficient hardware accelerator architecture based on this network for deployment on low-resource platforms. Furthermore, we present a brightness visibility restoration method to prevent brightness loss in dehazed images. To evaluate the performance of our method, extensive experiments were conducted, comparing it with various traditional and deep learning-based methods, including images with artificial synthesis and natural blur. The experimental results demonstrate that our proposed method excels in dehazing ability, outperforming other methods in comprehensive comparisons. Moreover, it achieves rapid processing speeds, with a maximum frame rate of 105 frames per second, meeting the requirements of real-time processing.