  Banding or false contour is an annoying visual artifact whose impact is even
more pronounced in ultra high definition, high dynamic range, and wide colour
gamut visual content, which is becoming increasingly popular. Since users
associate a heightened expectation of quality with such content and banding
leads to deteriorated visual quality-of-experience, the area of banding removal
or debanding has taken paramount importance. Existing debanding approaches are
mostly knowledge-driven. Despite the widespread success of deep learning in
other areas of image processing and computer vision, data-driven debanding
approaches remain surprisingly missing. In this work, we make one of the first
attempts to develop a deep learning based banding artifact removal method for
images and name it deep debanding network (deepDeband). For its training, we
construct a large-scale dataset of 51,490 pairs of corresponding pristine and
banded image patches. Performance evaluation shows that deepDeband is
successful at greatly reducing banding artifacts in images, outperforming
existing methods both quantitatively and visually.
