There are several algorithms and techniques used in apps to colorize black and white pictures, and they achieve realistic results by utilizing advanced image processing techniques and machine learning algorithms. One popular method is the use of deep learning models, such as convolutional neural networks (CNNs), to analyze the image and predict the missing color information. These models are trained on large datasets of color images, which allows them to learn the patterns and relationships between colors and objects in an image.
Another technique is the use of color transfer, which involves taking a color image and applying its color palette to a black and white image. This can be done using various algorithms, such as color matching, color gradient, and color histogram. Color matching involves finding the closest color match between the color image and the black and white image, while color gradient involves applying a color gradient to the black and white image based on the colors in the color image. Color histogram involves analyzing the color distribution of the color image and applying it to the black and white image.
In addition, some apps use a combination of these techniques, such as using a deep learning model to predict the color information and then applying color transfer to refine the results. Some apps also allow users to adjust the color settings and fine-tune the results to achieve the desired level of realism.
Overall, the algorithms and techniques used in apps to colorize black and white pictures are designed to produce realistic and natural-looking results by analyzing the image and applying advanced image processing techniques. They are constantly evolving and improving, allowing for more accurate and detailed colorization of black and white images.