![]() ![]() ![]() Experimental results show that our method largely outperforms the state-of-the-art methods. We evaluate our method on the Scene Flow, Cityscapes, Middlebury, and Sintel datasets. In addition, to correct wrongly colorized pixels in occlusion regions, we propose a color residue joint learning module to correct the colorization result with the input gray image as guidance. Explore more than 10 million color schemes perfect for any project Collect, manage and export your palettes with ease from your new dashboard Pro Profile, a new beautiful page to present yourself and showcase your palettes, projects and collections Get advanced PDF export options like shades, hues, color blindness, etc. The weight values between pixels in the input image and the reference image are obtained by learning a weight volume using deep feature representations, where an attention operation is proposed to focus on more useful candidate pixels and a 3-D regulation is performed to learn with context information. Based on our observation that, for each pixel in the input image, there usually exist multiple pixels in the reference image that have the correct colors, our method performs weighted average of colors of the candidate pixels in the reference image to utilize more candidate pixels with correct colors. In the monochrome-color dual-lens system, the gray image captured by the monochrome camera has better quality than the color image from the color camera. We propose a novel deep convolution network to solve the colorization problem in an end-to-end way. ![]() Related works usually use hand-crafted methods to search for the best-matching pixel in the reference image for each pixel in the input gray image, and copy the color of the best-matching pixel as the result. To get high-quality color images, it is desired to colorize the gray image with the color image as reference. In the monochrome-color dual-lens system, the gray image captured by the monochrome camera has better quality than the color image from the color camera, but does not have color information. Beijing University of Posts and Telecommunications ![]()
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