Mail me for a modified Apdrawing dataset. The highlight of the project is in producing synthetic data, thanks to pyimagesearch.com for wonderful blogs.

Black and white photography bears its emotional impact. You get rid of populist distractions or kitschy stereotypes of colors. The skies are blue, and the roses are red. But what is within us? The lights, the shadows, the contrasts.

Thousands of historical images from across Ireland are being brought to life in color for the first time, thanks to a new AI-led photo project. Combining digital technology with painstaking historical research, professors John Breslin and Sarah-Anne Buckley at the National University of Ireland, Galway, have been able to turn photos, originally shot in black in white, into rich color images. It includes portraits of key figures like Oscar Wilde and poet W.B. Yeats, as well as defining moments in history, like the Titanic setting sail from the Belfast shipyard where it was constructed. Yet, some of the most compelling photos depict everyday scenes -- people herding pigs, spinning wool or packed onto the back of horse-drawn carts. And while poverty is evident in pictures of barefoot villagers crowding around for a photo, or of Dublin's working-class tenement buildings, there are also well-to-do family shots and depictions of upper-class pastimes like fox hunting.

The main aim of the project is to create amazing line art portraits. Lets cartoonize the lineart portraits, its still in the making but have a look at some pretty pictures. The amazing results that the model has produced has a secret sauce to it. The initial model couldn't create the sort of output I was expecting, it mostly struggled with recognizing facial features. I wanted to break-in and produce results that could recognize any pose.

For years, production companies have used costly techniques to colorize old black and white movies. But now artificial intelligence is placing that capability in the hands of everyday users, allowing you to add color to old family photos, historical images, or black and white video frames in seconds. It works like this: A developer feeds a large number of color images into a neural network, which is AI-speak for software modeled after brain functions. Over time, the software learns to recognize different objects and determine their likely colors. These algorithms are incorporated into online services as well as software that you can download and run on a computer.

Image colorization is an engaging topic in the field of image-to-image translation. Even though color photography was invented in 1907, It didn't become popular for the average person until the 1960s because of its expensiveness and inaccessibility. All the photography and videography up until then was done on Black & White. Colorizing these images was impossible--until the DeOldify deep learning model came to life. DeOldify is an open-source deep learning model created by Jason Antic, used to add high-quality colorization to grayscale images with amazing results.

Colourisation or adding colours to the black and white or monochrome images and videos has witnessed a widespread adoption for a few decades now. Traditional colourisation techniques need a lot of human efforts as well as are costlier. However, with the advent of emerging technologies like artificial intelligence, these two major issues are disappearing slowly. Not only this but also we have witnessed how researchers are using deepfake techniques to swap faces of celebrities and other popular faces around the globe. Let's take a look at the few movies that have been coloured using artificial intelligence.

To start right away on your own machine with your own images or videos without training the models yourself, you'll need to download the "Completed Generator Weights" listed below and drop them in the /models/ folder. The colorization inference notebooks should be able to guide you from here. The notebooks to use are named ImageColorizerArtistic.ipynb,

Yes it was because of MSE loss! I'm still new to deep learning and not familiar with GANs so I tried to do it with a traditional auto-encoder CNN and ended up with that result. I will check your work later when I'm free for sure, it sounds like an interesting read