研究目的
Aiming at solving the problem of color distortion existing in the dark original pruning algorithm, an improved transmittance computation approach separated for each color channel is proposed.
研究成果
The proposed algorithm effectively reduces color distortion and improves defogging efficiency, achieving better visual effects and higher objective metrics compared to existing methods. It reduces running time by 4 to 10 times and provides higher color fidelity and image clarity. Future work should focus on eliminating the block effect and further optimizing the algorithm for broader applicability.
研究不足
The estimated dark channels are still involved in the calculation of the relationship of the transmittance, and there exists slight block effect in the restoration result. The algorithm may not be fully effective for all types of foggy images, such as those with large-scale scenes or mixed scales, and relies on assumptions about atmospheric uniformity.
1:Experimental Design and Method Selection:
The study uses an improved transmittance computation method based on Beer-Lambert law, analyzing the influence of incident light frequency on transmittance for each color channel. It involves down-sampling images to refine transmittance and then restoring to original size for efficiency.
2:Sample Selection and Data Sources:
Nine typical foggy images were selected for experiments, including images from the FRIDA2 dataset.
3:List of Experimental Equipment and Materials:
A PC with
4:3 GHz Core i5-7200 U@5Ghz processor, 8 GB RAM, 64-bit Windows 10 Operating System, and MATLAB software for coding and implementation. Experimental Procedures and Operational Workflow:
The procedure includes down-sampling the image, calculating dark channel and transmittance, refining transmittance using soft image matting, restoring transmittance to original size, calculating transmittance for all color channels, and recovering the image using the atmospheric scattering model.
5:Data Analysis Methods:
Qualitative and quantitative evaluations were conducted, including subjective visual assessment and objective metrics such as e (new added visible edge ratio), r (gradient ratio), H (color fidelity), PSNR, and SSIM.
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