A polarization-based image restoration method for both haze and underwater scattering environment

Abstract

Existing polarization-based defogging algorithms rely on the polarization degree or polarization angle and are not effective enough in scenes with little polarized light. In this article, a method of image restoration for both haze and underwater scattering environment is proposed. It bases on the general assumption that gray variance and average gradient of a clear image are larger than those of an image in a scattering medium. Firstly, based on the assumption, polarimetric images with the maximum variance (Ibest) and minimum variance (Iworst) are calculated from the captured four polarization images. Secondly, the transmittance is estimated and used to remove the scattering light from background medium of Ibest and Iworst. Thirdly, two images are fused to form a clear image and the color is also restored. Experimental results show that the proposed method obtains clear restored images both in haze and underwater scattering media. Because it does not rely on the polarization degree or polarization angle, it is more universal and suitable for scenes with little polarized light.

Adaptive wavelet transform defogging scheme for real-time video restoration with field programmable gate array implementation

“…2 Image defogging encompasses three main approaches: image enhancement, 3 convolutional neural networks, 4 and image recovery. [5][6][7] In recent years, image recovery has garnered increased attention, focusing on the retrieval of scene transmission maps based on atmospheric scatter models. 5 Addressing the challenge of low-visibility conditions in foggy weather, Narasimhan et al developed a physical imaging model grounded in atmospheric scattering phenomena.…”

“…[5][6][7] In recent years, image recovery has garnered increased attention, focusing on the retrieval of scene transmission maps based on atmospheric scatter models. 5 Addressing the challenge of low-visibility conditions in foggy weather, Narasimhan et al developed a physical imaging model grounded in atmospheric scattering phenomena. 6 Building upon this model, He et al introduced the dark channel algorithm, 7 which identifies regions where at least one color channel in non-sky areas contains pixels with low intensity, approaching zero.…”

“…Image defogging encompasses three main approaches: image enhancement, 3 convolutional neural networks, 4 and image recovery 5 – 7 In recent years, image recovery has garnered increased attention, focusing on the retrieval of scene transmission maps based on atmospheric scatter models 5 .…”