Prof. Yanwei Pang Profile

Prof. Yanwei Pang

at Tianjin Univ

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Publications (2)

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SPIE Journal Paper | 19 March 2023

Retinex decomposition and fusion dehazing network

Aiping Yang, Yaran Zhou, Jinbin Wang, Xiaoxiao Li, Jin Liu, Jiale Cao, Yanwei Pang

JEI, Vol. 32, Issue 02, 023014, (March 2023) https://doi.org/10.1117/12.10.1117/1.JEI.32.2.023014

KEYWORDS: Image fusion, Reflection, Light sources and illumination, Image restoration, Air contamination, Education and training, Error analysis, Visualization, Atmospheric modeling, Image quality

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Image dehazing has been widely used in vision-based fields, such as detection, segmentation, traffic monitoring, and automated vehicle system. However, most of the existing end-to-end dehazing networks are fully data driven without physical constraints or prior information guidance, leading to difficulties in exploring latent structures and statistical characteristics of hazy images. We propose a novel Retinex decomposition-fusion dehazing network consisting of a dual-branch decomposition module and a fusion optimization module. Different from existing solutions, we decompose the clear images in the commonly used RESIDE dataset based on Retinex theory to construct the clear illumination map and reflection map datasets to drive the network training, equivalently imposing reasonable constraints on the network and achieving impressive dehazing performances. The dual-branch decomposition module is developed to estimate the illumination map and the reflection map, respectively. The illumination map mainly contains the global features of the image, whereas the reflection map reflects the inherent color properties of the image and contains rich details, with which we explore the latent structures and statistical characteristics of hazy images. In addition, the dual-branch structure avoids the error accumulation and information cancellation existing in current methods. Subsequently, the estimated illumination map and reflection map are fused and refined via the fusion optimization module to access the dehazed image. Experiments show that the proposed network has better generalization and visual effects than existing fully data-driven methods and can be applied successfully to real-world scenarios.

SPIE Journal Paper | 20 July 2020

Detail-preserving single nighttime image dehazing

Aiping Yang, Jin Liu, Zhong Ji, Yanwei Pang

JEI, Vol. 29, Issue 04, 043010, (July 2020) https://doi.org/10.1117/12.10.1117/1.JEI.29.4.043010

KEYWORDS: Image restoration, Air contamination, Image fusion, Atmospheric modeling, Denoising, Image processing, Light sources, Lithium, Scattering

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Most existing dehazing approaches employ image priors such as a dark channel prior, which may be violated in the nighttime. In addition, these approaches perform dehazing directly on the original hazy image, which usually results in detail blurring and noise amplifying. To address both issues, an effective single nighttime image haze removal approach is presented. We first decompose the hazy image into a structure layer and a texture layer. Specifically, the dehazing operation is performed only on the structure layer to avoid detail loss and noise amplification. Due to the low frequency characteristics of the haze and the glow of artificial lights, the atmospheric veil is estimated on structure layer dehazing. Furthermore, to eliminate the nonuniform illumination effects, we present an ambient light estimation approach based on multiscale fusion. At the same time, the denoising is performed in the texture layer. Extensive experiments demonstrate that the proposed approach recovers a high-quality, haze-free, and noise-free image with vivid details.

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