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