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In this paper, we propose an effective approach for consistent tonal correction of multi-view images during mosaicking. Our methodis specifically designed for mosaicking multi-view remote sensing images acquired under different conditions and/or presentinginconsistent tone. To avoid the correlation of three channels in original RGB images, we convert them to an orthogonal color space lαβin advance. First of all, the tones of sequential images are transferred from an example image reasonably via our improved color transferalgorithm. Secondly, the more refined adjustments take place in the luminance channel l and color channels α and β, independently. Inthe luminance channel, the global gain compensation is applied to minimize the luminance difference between pairs of images by theleast square estimator. In the color channels, the specifically designed stepwise histogram adjustments make all the images consistenttone as a whole, including the initial correction transferring the color characteristics of the automatically selected reference subset toother images in an optimal order and the consistent correction readjusting each image by referring all their neighbors based on theoverlaps. Thirdly, we creatively transfer the original structures to the previously corrected images by a local linear model, which canpreserve the local structures of the original images. Finally, several groups of convincing experiments on both challenged synthetic andreal data demonstrate the validity of our proposed approach

CONSISTENT TONAL CORRECTION FOR MULTI-VIEW REMOTE SENSING IMAGE MOSAICKING