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We present a novel color-aware perceptual (CAP) loss for learning the task ofpan-sharpening. Our CAP loss is designed to focus on the deep features of apre-trained VGG network that are more sensitive to spatial details and ignorecolor information to allow the network to extract the structural informationfrom the PAN image while keeping the color from the lower resolution MS image.Additionally, we propose "guided re-colorization", which generates apan-sharpened image with real colors from the MS input by "picking" the closestMS pixel color for each pan-sharpened pixel, as a human operator would do inmanual colorization. Such a re-colorized (RC) image is completely aligned withthe pan-sharpened (PS) network output and can be used as a self-supervisionsignal during training, or to enhance the colors in the PS image during test.We present several experiments where our network trained with our CAP lossgenerates naturally looking pan-sharpened images with fewer artifacts andoutperforms the state-of-the-arts on the WorldView3 dataset in terms of ERGAS,SCC, and QNR metrics.

Pan-Sharpening with Color-Aware Perceptual Loss and Guided Re-Colorization