In this paper, we consider the color-plus-mono dual-camera system and propose an end-to-end convolutional neural network to align fuse images from it in efficient cost-effective way. Our method takes cross-domain cross-scale as input, consequently synthesizes HR colorization results facilitate trade-off between spatial-temporal resolution color depth single-camera imaging system. contrast previ...

We propose a novel approach for generating high quality visible-like images from Synthetic Aperture Radar (SAR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on a cascaded network of convolutional neural nets (CNNs) for despeckling and image colorization. The cascaded structure results in faster convergence during training and...

We propose a colorization tool which utilizes both minimal user input and traditional convolutional neural networks to color gray-scale images. Deep learning can only go so far in solving colorization. Our major contribution is to show how we can condense color information into a low dimensional color palette and be able to infer this color palette from minimal user input. This allows an intera...

We consider the colorization problem of grayscale images when some pixels, called scribbles, with initial colors are given. In this paper, we propose a new multi-layer graph model and an energy formulation that can incorporate higher-order cues for reliable colorization of natural images. In contrast to most existing energy functions with unary and pairwise constraints, we address the problem o...

Colorization is an automatic technique to enhance greyscale images by introducing chromatic information. In this research we investigate to produce colorized medical images, potentially supporting in better understanding of anatomy, anomalies and infections. Begins with proposed mandatory preprocessing steps for medical images noise removal and edge improvement, followed by colorization process...

This paper proposes a new algorithm for coloring a monochrome image. Recently, a few effective colorization algorithms have been proposed. Those algorithms can colorize monochrome images by given some color hint, and they work well as an intuitive impression. However, a realistic colorization was difficult by those algorithms, because they have not used any physical model. This paper focuses on...

This paper presents a novel approach to scribblebased image colorization. In the work reported here we have explored how to exploit the textural information to improve this process. For every scribbled image we extract the most discriminative features using linear discriminant analysis (LDA). After that, the whole image is projected onto a discriminative textural feature space. Our main contrib...

The identification of brain infarct in computed tomography (CT) images is difficult due the nature appearance of the infarct tissues similar to the normal tissues in the brain CT images. In this paper, a histogram-based colorization method is presented in order to enhance the visualization and interpretation of brain CT images. The presented is aimed to improve the diagnosis of brain CT images,...

We propose a novel approach to automatically produce multiple colorized versions of a grayscale image. Our method results from the observation that the task of automated colorization is relatively easy given a low-resolution version of the color image. We first train a conditional PixelCNN to generate a low resolution color for a given grayscale image. Then, given the generated low-resolution c...