Grayscale Image Colorization Using Deep Learning: A Case Study

Colorization of grayscale images is a hot topic in computer vision. Previous research mainly focuses on producing a color image to recover the original one in a supervised learning fashion. However, since many colors share the same gray value, an input grayscale image could be diversely colorized while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test on 80 humans further indicates our generated color schemes are highly convincible.

Colorization of grayscale images is a challenging and fascinating problem in computer vision. This project leverages deep learning techniques, specifically convolutional neural networks (CNNs), to automatically convert grayscale images into colorized versions. Utilizing OpenCV and pre-trained deep learning models such as DeOldify or the Colorful Image Colorization model, we achieve high-quality image colorization with minimal user intervention. The model learns color distributions from large datasets and intelligently predicts realistic colors for grayscale images. This approach finds applications in restoring old photographs, enhancing medical imaging, and improving visual aesthetics in various fields.

Visual communication concepts enable linguistics or semiotics to the teaching of visual communication designs, creating graphic designs into an innovative and scientific discipline. The use of storyline techniques in visual communication not only inspires the imagination of designer but also arouses the visual memory of the audience. Besides, improving cultural heritage such as historical images is important to protect cultural diversity. Recently, the developments of deep learning (DL) and computer vision (CV) approaches make it possible for the automatic colorization of grayscale images into color images. Also, the usage of visual communication design in APP interface design has increased. With this motivation, this work introduces the enhanced deep learning-based automated historical image colorization (EDL-AHIC) technique for wireless network-enabled visual communication. The proposed EDL-AHIC technique intends to effectually convert the grayscale images into color images. The presented EDL-AHIC technique extracts the local as well as global features. For global feature extraction, the enhanced capsule network (ECN) model is applied. Finally, the fusion layer and decoding unit are employed to determine the output, i.e., chrominance component of the input image. A comprehensive experimental validation process is performed to ensure the betterment of the EDL-AHIC technique. The comparison study reported the supremacy of the EDL-AHIC technique over the other recent methods.

Automatic image colorization is to generate a colorful image from a given gray image automatically. It is an ill-conditioned task and remains a challenging problem in computer vision. One main challenge in example-based image colorization is how to find the correct correspondence between the grayscale image and the reference color image. In this paper, we propose a novel automatic example-based image colorization method via weighted sparse matching. First, we segment the images into superpixels, and operates at the superpixel level rather than pixels. Then we extract intensity features and texture features for each superpixel, which are then concatenated to form its descriptor. The feature descriptors collected from the reference image composes the representation dictionary. Finally, the correspondence between target grayscale image and the colorful reference image is built by solving a weighted sparse representation learning problem, and the target superpixels are colorized based on the chrominance information from the corresponding reference superpixels. Experimental results demonstrate that our colorization method outperforms several state-of-the-art methods.

At present, deep learning method is widely used in the field of gray image colorization. Qinghai farmer painting has distinct national characteristics. The farmer painting has bright colors, high saturation, chaotic color distribution and low color contrast, so it is difficult to restore the image color with high fidelity by using the general deep learning image colorization method. The Pix2Pix generation adversarial network of grayscale image colorization method uses the Leaky ReLU function as the activation function. The proposal algorithm replaces the maximum pooling layer with the convolution layer to retain more image feature information and further to improve the color simulation effect. Meanwhile, in view of the lack of relevant Qinghai farmer painting data set, the data set of Qinghai farmer paintings is constructed to meet the needs of network training. The experimental results show that the improved method further improves the color effect and can generate high quality color images of Qinghai farmer paintings with more real color distribution.

Image colorization is of great use for several applications, such as the restoration of old images, as well as enabling the storage of grayscale images, which take up less space, which can later be colorized. But this problem is hard since there exist many possible color combinations for a particular grayscale image. Recent developments have aimed to solve this problem using deep learning. But, for achieving good performance, they require highly processed inputs, along with additional elements, such as semantic maps. In this paper, an attempt has been made for generalizing the procedure of colorization using a conditional Deep Convolutional Generative Adversarial Network (DCGAN) by adding Perceptual Loss. The network is trained over the CIFAR-100 dataset. The results of the proposed generative model with perceptual loss are compared with the existing state-of-the-art systems normal GAN model and U-Net Convolutional model.

The science of deep learning has undergone a revolution thanks to the invention of generative adversarial networks (GANs), a kind of deep learning design that can produce synthetic data that is equivalent to real-world data. GANs have recently been employed for a number of images processing operations, including photo colorization. The storing of grayscale photos, which require less storage space and can then be colored, and the restoration of old photographs are two of the key uses for image colorization. Although there are numerous color choices that can be employed with a specific grayscale image, this problem is challenging. Deep learning has been used in recent advances to make an effort to overcome this issue. In addition to the generator network, the Pix2Pix architecture also has a discriminator network that tries to tell bogus from real color images. Many datasets, including the CIFAR-10 and COCO datasets, have shown that the suggested method produces high-quality colorized images that closely resemble the ground truth images. Overall, this study shows how well GANs–and specifically the Pix2Pix architecture–can be used to colorize images. According to the research, this technology might be applied to a number of activities, including the processing of images and videos, digital restoration, and aesthetic representation.

Gray Scale image colorization is an appealing area in the world of image processing. This work presents a simple process for colorizing gray image, using a colored image which is similar to this gray scale image but not the colorized version of the gray image. Here we convert both these images to a decorrelated color space YCbCr and then divide these images into small windows of equal size, then we calculate mean and standard deviation for data points based on luminance values in each of these windows to compute a luminance measure and we also extract texture features like energy, entropy, contrast, correlation and homogeneity based on correlation matrix for each of these windows. We apply proper weights to these texture features to calculate a texture similarity measure & then calculate a similarity measure based on the texture similarity measure and luminance measure for each of these windows in both the images. Then we compare each window in gray scale image with every other window in colored image using this similarity measure and find the best matching window. Then the chromatic properties of this colored window are transferred to the gray window pixel by pixel and colorization of gray scale image is achieved, which produces believable results.

There is no exact solution for colorization of grayscale images. In the initial work done [1], color traits transfer techniques to color grayscale images are proposed. The main focus of the techniques [1] is to minimize the human efforts needed in manually coloring the grayscale images. The human interaction is needed only to find a reference color image, then the job of transferring color traits from reference color image to grayscale image is done by proposed techniques. This paper is an extension to [14]. Here the technique of color traits transfer to grayscale images have been performed with respect to YUV color space along with different pixel window sizes. The pixel window sizes used are total fifteen ranging from window with size 1×2 to 4×4. So in all 15 different versions of color traits transfer to grayscale algorithms are tested over thirty-one different images for deciding the color space and pixel window size giving best quality of coloring. The experimental results gives that the pixel window of size 4×3 suits the best in YUV color space.

Image colorization is the process of adding color to an originally black and white image. This means the artist needs to plan the color scheme and then spend time painstakingly filling in the colors manually. The current tool of choice is Photoshop or an equivalent. A single picture can take up to one month to colorize. In this chapter, we will implement a simple convolutional neural network (CNN) model to understand how image colorization works.

Image colorization estimates RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality. Over the last decade, deep learning techniques for image colorization have significantly progressed, necessitating a systematic survey and benchmarking of these techniques. This article presents a comprehensive survey of recent state-of-the-art deep learning-based image colorization techniques, describing their fundamental block architectures, inputs, optimizers, loss functions, training protocols, training data, etc. It categorizes the existing colorization techniques into seven classes and discusses important factors governing their performance, such as benchmark datasets and evaluation metrics. We highlight the limitations of existing datasets and introduce a new dataset specific to colorization. We perform an extensive experimental evaluation of existing image colorization methods using both existing datasets and our proposed one. Finally, we discuss the limitations of existing methods and recommend possible solutions and future research directions for this rapidly evolving topic of deep image colorization. The dataset and codes for evaluation are publicly available at https://github.com/saeed-anwar/ColorSurvey.

Colorization: Converting a grayscale image into an image with colors acceptable to the viewer and achieving the minimum coloration goal until it becomes closer to reality. The goal of the coloring process is to produce a convincing image and the result is close to the reality of the colored image. Converting grayscale images to color images is a very difficult process due to the specificity of grayscale images and how closely they match color in every detail of the image. Coloring methods have evolved to become less invasive and produce more acceptable results to the viewer's eye, but they have become more difficult. This research provides an overview of image colorization methods, their advantages and disadvantages, and compares the quality of each method according to specific criteria. Coloring methods have evolved from primitive methods based on the manual principle to using computing techniques such as adding scribbles within areas of the image, then moving to choosing a reference image that can be used to obtain colors, to using deep learning techniques that rely on training a model on colored images and then testing this model and showing Methods based on deep learning have better results than other methods, and the algorithm showed GAN outstanding results in coloring that outperform some deep learning coloring methods, and also outperform other methods. We hope that this study will be a scientific product that will benefit the research community and enable them to obtain a useful overview of the most important methods of coloring images.

From a colourimetric point of view, colour has two independent aspects: brightness and chromaticity. In black and white images, all elements are of the same chromaticity and can be distinguished only by brightness contrast. In the colour image, elements of the same brightness can be discriminated by chromaticity (colour) contrast. Generally, colour image elements can be discriminated both by brightness and by chromaticity. As the human eye can distinguish a number of hues two orders of magnitude larger than the number of grey levels, it is safe to say that the colour image is much more informative than the black and white image. There are some peculiarities of the colour image and methods of its formation in SEM. Two principles of image formation are used. The first consists of the formation of a real colour image in the cathodoluminescence mode. In this case the colour of an image element is determined by the spectrum of the luminescence emission excited in the corresponding point of an object by the electron beam. The second principle is that of colour coding (quasicolour, pseudocolour), when a video signal in colour (either digital or analog) corresponds to a video signal (amplitude, frequency, phase, etc.) produced by any mode in the scanning electron microscope. We present a review of the methods of colour display of video information in scanning electron microscopy and their applications to physics, geology, soil science, biology, and medicine.

Color in any image playing a vital role for better understanding and real image visualization. In past when there was no concept of colored images each black and white image need to highlight its own merits to explain an image in a better way, but when the concept of colored images started the things were totally changed to describe and highlight key features and attributes of an image in a more better and effective manner. The key issue is that the old images and videos are color less and in some cases it needs to be colorized. There are a number of applications are uses to colorized a gray-scale image. The authors of this paper also presented an efficient deep learning based image colorization technique. The proposed system maps all the gray contrast pixels of the image into its corresponding colored pixels to produce a colored image using Convolutional Neural Network (CNN) technique. The proposed system is totally an automated system and it avoid any manual work or user hand-define rules. The system first read the gray contrast of each pixel of image; matches function then create the corresponding color contrast from the color map and finally convert the gray contrast image into color image. The system tested around 100 images and found accuracy about 95%.

High-speed color digital imaging devices are desired for many applications, including civilian and military missions. To capture color images a color sensor has to divide the incidence light into three spectral bands: blue, green and red. Due to the limitation of the spectral bandwidth, the speed of a color digital sensor is significant slower than that of a black and white sensor covering the entire visible bandwidth or the entire bandwidth from visible to near infrared. This paper introduces the conception of a high-speed color imaging device which can significantly increase the imaging speed without losing image resolution and other image qualities. This device uses the concept of black and white imaging to increase the imaging speed while keeping the image resolution unchanged. It also uses the concept of color imaging to capture color information, but at a significantly lower resolution to allow more incidence light being received by each sensor detector (pixel) to increase the imaging speed. The final color image at the desired resolution is recovered by integrating the spatial information from the black and white image and the color information from the low-resolution color image. An automatic image fusion technique is used to integrate the spatial information and the color information. The quality of the image fusion technique plays a crucial role for quality of the recovered color image. The concept of this high-speed color imaging device has been tested with available low-resolution color images and high-resolution black and white images from the latest commercial satellites, Ikonos and QuickBird. The test results demonstrated that the concept of the high-speed digital color imaging device is promising for producing real high-speed color digital devices for practical applications.