Automatically eliminating seam lines with Poisson editing in complex relative radiometric normalization mosaicking scenarios
This addresses a domain-specific problem for remote sensing image processing, offering an incremental improvement by automating seam removal in complex scenarios where traditional methods fail.
The paper tackles the problem of seam lines and radiometric contrast in relative radiometric normalization mosaicking of remote sensing images, which degrade visual quality and downstream task accuracy, by proposing an automatic method that uses histogram matching and Poisson editing to eliminate these artifacts, showing visual superiority over existing automatic and manual methods in experiments.
Relative radiometric normalization (RRN) mosaicking among multiple remote sensing images is crucial for the downstream tasks, including map-making, image recognition, semantic segmentation, and change detection. However, there are often seam lines on the mosaic boundary and radiometric contrast left, especially in complex scenarios, making the appearance of mosaic images unsightly and reducing the accuracy of the latter classification/recognition algorithms. This paper renders a novel automatical approach to eliminate seam lines in complex RRN mosaicking scenarios. It utilizes the histogram matching on the overlap area to alleviate radiometric contrast, Poisson editing to remove the seam lines, and merging procedure to determine the normalization transfer order. Our method can handle the mosaicking seam lines with arbitrary shapes and images with extreme topological relationships (with a small intersection area). These conditions make the main feathering or blending methods, e.g., linear weighted blending and Laplacian pyramid blending, unavailable. In the experiment, our approach visually surpasses the automatic methods without Poisson editing and the manual blurring and feathering method using GIMP software.