no code implementations • 27 Apr 2024 • Zhongze Wang, Haitao Zhao, Jingchao Peng, Lujian Yao, Kaijie Zhao
ODCR aims to ensure that the haze-related features of the dehazing result closely resemble those of the clear image, while the haze-unrelated features align with the input hazy image.
no code implementations • 26 Jul 2023 • Zhongze Wang, Haitao Zhao, Lujian Yao, Jingchao Peng, Kaijie Zhao
In LB, we explore local density features from the dehazing residuals between hazy inputs and proposal images and introduce an Intermediate Dehazing Residual Feedforward (IDRF) module to update local features and pull them closer to clear image features.
no code implementations • 7 Jun 2023 • Lujian Yao, Haitao Zhao, Jingchao Peng, Zhongze Wang, Kaijie Zhao
We first introduce a Focus module employing bidirectional cascade which guides low-resolution and high-resolution features towards mid-resolution to locate and determine the scope of smoke, reducing the miss detection rate.
no code implementations • 11 Jan 2023 • Jingchao Peng, Haitao Zhao, Kaijie Zhao, Zhongze Wang, Lujian Yao
Background reconstruction is one of the methods to deal with this problem.
no code implementations • 28 Sep 2022 • Jingchao Peng, Haitao Zhao, Kaijie Zhao, Zhongze Wang, Lujian Yao
To deal with this difficulty, this paper proposes a neural-network-based ISTD method called CourtNet, which has three sub-networks: the prosecution network is designed for improving the recall rate; the defendant network is devoted to increasing the precision rate; the jury network weights their results to adaptively balance the precision and recall rate.