Introduction Content-aware fill is a powerful tool designers deep learning in python pdf photographers use to fill in unwanted or missing parts of images. Image completion and inpainting are closely related technologies used to fill in missing or corrupted parts of images. There are many ways to do content-aware fill, image completion, and inpainting. We’ll approach image completion in three steps.

We’ll first interpret images as being samples from a probability distribution. This interpretation lets us learn how to generate fake images. Then we’ll find the best fake image for completion. Photoshop example of automatically filling in missing image parts. Photoshop example of automatically removing unwanted image parts.

Completions generated by what we’ll cover in this blog post. The centers of these images are being automatically generated. The source code to create this is available here. I selected a random subset of images from the LFW dataset. Step 1: Interpreting images as samples from a probability distribution How would you fill in the missing information? In the examples above, imagine you’re building a system to fill in the missing pieces. How do you think the human brain does it?

What kind of information would you use? Contextual information: You can infer what missing pixels are based on information provided by surrounding pixels. Without contextual information, how do you know what to fill in? Without perceptual information, there are many valid completions for a context. It would be nice to have an exact, intuitive algorithm that captures both of these properties that says step-by-step how to complete an image.

Creating such an algorithm may be possible for specific cases, but in general, nobody knows how. Today’s best approaches use statistics and machine learning to learn an approximate technique. But where does statistics fit in? To motivate the problem, let’s start by looking at a probability distribution that is well-understood and can be represented concisely in closed form: a normal distribution. Let’s sample from the distribution to get some data. Make sure you understand the connection between the PDF and the samples.

This is a 1D probability distribution because the input only goes along a single dimension. We can do the same thing in two dimensions. PDF and samples from a 2D normal distribution. The PDF is shown as a contour plot and the samples are overlaid. The key relationship between images and statistics is that we can interpret images as samples from a high-dimensional probability distribution. The probability distribution goes over the pixels of images. Imagine you’re taking a picture with your camera.

This picture will have some finite number of pixels. When you take an image with your camera, you are sampling from this complex probability distribution. In this post, we’ll use color images represented by the RGB color model. So how can we complete images? Let’s first consider the multivariate normal distribution from before for intuition. This concept naturally extends to our image probability distribution when we know some values and want to complete the missing values. Just pose it as a maximization problem where we search over all of the possible missing values.