Abstract
The scattering and absorption of light propagating underwater cause the underwater images to present low contrast, color deviation, and loss of details, which in turn make human posture recognition challenging. To address these issues, this study introduced the dual-guided filtering technique and developed an underwater diver image improvement method. First, the color distortion of the underwater diver image was solved using white balance technology to obtain a color-corrected image. Second, dual-guided filtering was applied to the white balanced image to correct the distorted color and enhance its details. Four feature weight maps of the two images were then calculated, and two normalized weight maps were constructed for multi-scale fusion using normalization. To better preserve the obtained image details, the fusion image was histogram-stretched to obtain the final enhanced result. The experimental results validated that this method has improved the accuracy of underwater human posture recognition.
Highlights
Computer vision technology is developing rapidly and is continuously being applied in new fields, including human-centered applications
Our method first compensated for the red channel, performed gray world white balance to alleviate the color distortion of the image, and obtained the detailenhanced and sharpened images based on guided filtering
This paper presents an underwater diver image enhancement approach based on doubleguided filtering
Summary
Computer vision technology is developing rapidly and is continuously being applied in new fields, including human-centered applications. An improved guided filter was used to optimize the transfer map Despite these various underwater image restoration methods, accurately estimating various parameters of the optical image model is still difficult because of the complexity of the underwater environment and illumination conditions. In 2012, Ancuti et al [12] built a fusion-based model for image enhancement using the Laplacian contrast weight, local contrast weight, and saliency weight This multi-scale fusion pyramid strategy obtained good results; the images had improved sharpness, contrast, and color distribution. This study proposed an underwater diver image improvement method with dualguided filtering This method ensured correction of the diver image’s color and enhanced the image’s details and contrast. The results showed that the approach introduced could accurately correct the color of underwater diver images, enhance detailed information, and improve the effectiveness of human posture recognition.
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