Underwater images suffer color distortions and low contrast. This is because the light is absorbed and scattered when it travels through water. Different underwater scenes result in different color deviations and levels of detail loss in underwater images. To address these issues of color distortion and low contrast, an underwater image enhancement method that includes two-level wavelet decomposition maximum brightness color restoration, and edge refinement histogram stretching is proposed. First, according to the Jaffe-McGlamery underwater optical imaging model, the proportions of the maximum bright channel were obtained to correct the color of underwater images. Then, edge refinement histogram stretching was designed, and edge refinement and denoising processing were performed while stretching the histogram to enhance contrast and noise removal. Finally, wavelet two-level decomposition of the color-corrected and contrast-stretched underwater images was performed, and the decomposed components in equal proportions were fused. The proposed method can restore the color and detail and enhance the contrast of the underwater image. Extensive experiments demonstrated that the proposed method achieves superior performance against state-of-the-art methods in visual quality and quantitative metrics.
PurposeThe purpose of this study is to analyze the short-term development pattern and long-term development trend of the digital supply chain.Design/methodology/approachThis study uses the combination of short-term game and long-term evolutionary game theory.FindingsFindings of this study suggest that irrational decisions can make the evolutionary path of the digital supply chain complex and unpredictable.Originality/valueThis study proposes an evolutionary game model for the digital supply chain that can provide good guidance for the digitalization process of enterprises.
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.
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