This study represents an innovative automatic method for black and white films colorization using texture features and a multilayer perceptron artificial neural network. In the proposed method, efforts are made to remove human interference in the process of colorization and replace it with an artificial neural network (ANN) which is trained using the features of the reference frame. Later, this...

A novel example-based process for automated colorization of grayscale images using texture descriptors (ACTD) without any human intervention is proposed. By analyzing a set of sample color images, coherent regions of homogeneous textures are extracted. A multi-channel filtering technique is used for texture-based image segmentation. For each area of interest, state of the art texture descriptor...

x 1. INTRODUCTION 1 1.1 Medical Imaging 1 1.2 Medical Image Segmentation 4 1.3 Digital Image Processing 7 1.4 Motivation 9 1.5 Thesis Organization 10 2. LITERATURE REVIEW 12 2.1 Segmentation 12 2.1.1 Classical Approaches 13 2.1.2 Advanced Approaches 14 2.1.2.1 Statistical and Probabilistic Approaches 14 2.1.2.2 Deformable Models 15 2.1.2.3 Artificial Neural Networks (ANNs) 16 2.1.2...

An adaptive image colorization algorithm based on total variation and partial differential equations (PDE) is proposed to overcome problems of color blurring near edges in some colorization algorithms. A partial differential equation is established through minimization of a functional. An adaptive method is then used to select the model parameters. By choosing suitable diffusion coefficients re...

Colorization and illumination are key processes for creating animated cartoons. Computer assisted methods have been incorporated in animation/illustration systems to reduce the artists' workload. This paper presents a new method for illumination and colorization of 2D drawings based on a region- tree representation. Starting from a hand-drawn cartoon, the proposed method extracts geometric and ...

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...