search
top

Image Colorization

Colorization is a technique of transferring color to grayscale, sepia or monochromatic images. The term Colorization is patented by Wilson Markle in 1970 to describe the computer-assisted process he invented for adding color to black and white movies or TV programs. After that, different trends for grayscale coloring were appeared since 80s.

 
 

Introduction

 
Say cheese…!! 😁 Who doesn’t love taking photos?? We are currently in selfie era where people like to capture every little moment. Today the black and white filter we apply in our photo was actually one of the histories of photography. Isn’t it astonishing that today we apply black and white filter despite of having colorful photo, while decades prior people were desperately trying to add colors to photo. So, let’s see the journey of Black and white past to high resolution colorful present and how the colors have brought new life to your old beautiful memories.
 
Colorization is the process of adding colors to black and white image. The ability to capture color came in 1851 from a minister Levi Hill. he used the daguerreotypes process yet that process lacked the ability to reproduce color. What’s more many people were skeptical about that process.
 
Minster Levi Hll

Source:The Minster Levi Hill

 

Colorization

 
Digital image is considered to be a discrete function I(x,y), where x and y are spatial coordinates, and the amplitude of I at any pair of coordinates (x,y) is called the intensity or the gray level of the image at that point. Each point in the image is called pixel and is signified by its position and intensity. For a color image, any pixel color is defined by a values vary from 0 to 255, portraying the red, green and blue color components of this pixel. This type of image is sometimes called as a RGB image.
 

  • True color image: It represents natural color of any objects. also known as an RGB image, it is an image in which every pixel is indicated by three values – red, blue, and green components of the pixel’s color. The color of each pixel is determined by the combination of the red, green, and blue intensities stored in each color plane at the pixel’s location. Each color channel is represented by a single byte (8 bits), giving 256 discrete levels of each color channel. Each pixel is therefore represented by 3 bytes, or 24 bits, mostly 24 bit RGB image is used.
  •  

  • Binary image: In a binary image, each color pixel is represented by a single bit that means to the color as black or white.
  •  

  • Intensity image or a gray scale image: A grayscale image contains only shades of grays, much like a black and white photograph or TV movie. It’s sometimes called a monochrome image. Simply when the red, green, and blue values are equal it is considered as gray image, so each pixel in a grayscale image is represented by a single value, representing the gray level from 0 for black right through to 255 for white. Lowest intensity value or its absence in image is represented with black color and highest value represents white color. All values between this highest and lowest value represents the shades of gray.we can consider a gray-scale image as luminance channel Y of an RGB image. The Y channel is defined as a weighted average of the RGB channels:
  • Y = 0.299R + 0.587G + 0.114B

     

  • Pseudo color image: A pseudo color image is derived from a gray scale image by mapping each intensity value to a color according to a table or function. In this type, each pixel has an index referring to a RGB color value in attached color map. It represents false colors.

 

Colorization Problem

 
Gray image “colorization” means to give colors to gray images. It increases the visual appeal of images such as old black and white photos, movies. Coloring gray scale images means addition of chromatic values to gray image by regeneration of chromatic values for pixels in colors. This regeneration of chromatic values according to gray image is a problem. We are not able to generate exact information of chromatic values. Basically, color image consists of three-dimensional information about the color of image. RED, BLUE, GREEN. Whereas gray scale image consists of luminance and hence it is one dimensional.Converting color image to grey scale image means we are dropping information about color. It is easy to convert color image to grey scale image but reverse is not easy.
 
The colorization techniques can be classified under four categories: Hand coloring, Semi-automatic, Advance semi-automatic and fully advanced automatic coloring
 

 
Pseudo coloring: The idea is to perform 3 transformations on a particular gray level and feed this to the three color inputs (RGB) of a color monitor. The result is a composite image whose color content depends on the gray level to color transformations. In some cases, there is no color concept for gray scale image, but we can assign false color to image. The choice of the colormap is commonly determined by human decision. This color map is also called ‘Look Up Table’ (LUT).

 

Application Areas of Colorization

 
Colorization techniques are widely used is astronomy, MRI scans, and black-and-white
image restoration, Airport System, Satellite and Medical Imaging. Colorization didn’t stop to enhance the images only, but also the trend to make old productions more attractive by the cinema industry was the goal of many researchers over the last decades.

 

Re-colorization

 
Image re-colorization is a process of creating new synthetic images for given reference images and input images. It improves the visual perception of an image for design and artistic purposes. Contrasted with individuals with normal color vision, people with color vision deficiency (CVD) experience issues in recognizing certain blends of colors. This may obstruct visual communication owing to the increasing use of colors in recent years. To address this issue, re-color the image to preserve visual detail when perceived by people with CVD.

 

Decolorization

 
The persistent advancement in the techniques of coloring has improved the coloring results. This has driven the research to discover more coloring applications. Researchers began looking for new methods to dispose the colors from color images while retaining some information of their genuine qualities in order to restore color images with exceptionally high quality.
 
So at long last in the wake of experiencing the past to present, we can see an abundance of photographic images, from antique photography to low-resolution video, which lack color information. Assigning color to a black-and-white image is a profoundly ill-posed problem; given a certain image, there is often no “correct” feasible color. There are many more colorization algorithms like: Interactive Local Color Transfer between Images (ICT), Converting Grey-Scale Image to Color Image (CTGI), Pseudo-coloring with Histogram Interpolation (PHI), Color Transfer To Grayscale Images Using Texture Spectrum (CTTS), Colorization of Grayscale Images Using Fully Automated Approach (CGFA) etc.
 
I hope this brings you a little insight in colorization methods. Happy Coloring…!! 😁

8 Responses to “Image Colorization”

  1. Hetvi Julasana says:

    It’s colorblowing blog with good information.👏

  2. Parth Patadiya says:

    Intresting 👌
    It’s really amazing to apply this techniques to colorize old historical images and videos.

  3. Palak doshi says:

    Great work pankti🤩🤩 Keep it up

  4. Pinky Patel says:

    Nice Pankti….Thanks For Helping Our Project….

Leave a Reply

Your email address will not be published. Required fields are marked *

top