ﻻ يوجد ملخص باللغة العربية
Compared to color images captured by conventional RGB cameras, monochrome images usually have better signal-to-noise ratio (SNR) and richer textures due to its higher quantum efficiency. It is thus natural to apply a mono-color dual-camera system to restore color images with higher visual quality. In this paper, we propose a mono-color image enhancement algorithm that colorizes the monochrome image with the color one. Based on the assumption that adjacent structures with similar luminance values are likely to have similar colors, we first perform dense scribbling to assign colors to the monochrome pixels through block matching. Two types of outliers, including occlusion and color ambiguity, are detected and removed from the initial scribbles. We also introduce a sampling strategy to accelerate the scribbling process. Then, the dense scribbles are propagated to the entire image. To alleviate incorrect color propagation in the regions that have no color hints at all, we generate extra color seeds based on the existed scribbles to guide the propagation process. Experimental results show that, our algorithm can efficiently restore color images with higher SNR and richer details from the mono-color image pairs, and achieves good performance in solving the color bleeding problem.
In this paper, we present a fast exemplar-based image colorization approach using color embeddings named Color2Embed. Generally, due to the difficulty of obtaining input and ground truth image pairs, it is hard to train a exemplar-based colorization
Colorization has attracted increasing interest in recent years. Classic reference-based methods usually rely on external color images for plausible results. A large image database or online search engine is inevitably required for retrieving such exe
In this paper, we present a novel approach that uses deep learning techniques for colorizing grayscale images. By utilizing a pre-trained convolutional neural network, which is originally designed for image classification, we are able to separate con
Benefitting from insensitivity to light and high penetration of foggy environments, infrared cameras are widely used for sensing in nighttime traffic scenes. However, the low contrast and lack of chromaticity of thermal infrared (TIR) images hinder t
Underwater images suffer from color casts and low contrast due to wavelength- and distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided