An improved color consistency optimization method based on the reference image contaminated by clouds

Optimizing color consistency across multiple images is a crucial step in creating accurate digital orthophoto maps (DOMs). However, current color balance methods that rely on a reference image are susceptible to cloud and cloud shadow interference, making it challenging to ensure color fidelity and a uniform color transition between images. To address these issues, an improved method for color consistency optimization has been proposed to enhance image quality using optimized low-resolution reference images. Initially, the original image is utilized to reconstruct areas affected by clouds or cloud shadows on the reference image. For seamless cloning, a Poisson blending algorithm is employed to minimize color differences between reconstructed and other regions. Subsequently, based on a weighting approach, the high-frequency information obtained through Gaussian and bilateral filtering is superimposed to smooth the image boundary and ensure color continuity between images. Finally, local linear models are constructed to correct image color based on the optimized reference and down-sampled images. To validate the robustness of this approach, we tested it on two challenging datasets covering a wide area. Compared to state-of-the-art methods, our approach offers significant advantages in both quantitative indicators and visual quality.

Automated summary

An improved method for optimizing color consistency across multiple images is proposed, which utilizes optimized low-resolution reference images to enhance image quality. The method includes reconstructing affected areas, minimizing color differences, smoothing the image boundary, ensuring color continuity, and correcting image color based on optimized reference and down-sampled images. The approach shows significant advantages in both quantitative indicators and visual quality compared to state-of-the-art methods.