What is the Place of Deep Learning in Image Processing?

Deep learning is among the most advanced image processing tech concepts right now. You may beg to differ, recalling the “chihuahua and cupcakes” case where a DL-based system just couldn’t resolve objects the right way. However, when it comes to image scaling and resolution customization, the technology can do wonders.

What is the essence of software solutions for image processing based on DL. The place of deep learning in this niche exactly? Let’s figure out all these basics.   

GAN & Graphic Object Extrapolation

Generative adversarial network or GAN is an autonomous machine learning concept introduced by Google specialists in 2014. It is based on two interconnected networks – generative and discriminative. These are, basically, two algorithms – one generates a data sample while another tries to resolve it (i.e., distinguish true, correct data distribution results from the generator-produced candidates. 

The ultimate goal of these algorithms is to implement extrapolation (image outpainting), which is a complex to realize procedure that, however, boasts some excellent results when it comes to image processing and, particularly, scaling.

What do we ultimately get as a result of the GAN-based image processing? The software gets to expand an image most properly because equal duplicated parts are added to the recognized elements during the processing. At the end of it all, we get smooth lines and no image distortion whatsoever in most cases.

Processing Failure

The process of extrapolation doesn’t involve existing data – the software autonomously “comes up” with what can be placed beyond the boundaries of the existing image. That’s why certain system errors may appear when extensive detalization is required. One such particular GAN-related example of processing failure is when someone tries to clearly recognize blurred and distant faces in the surveillance camera zoomed footage.

Note also that up to date, GAN-based tools are used not only for the seemingly simple purpose of enlarging an image. They can also help generate new images based on the existing input data. For instance, you can implement face aging based on real user photos similar to the renowned FaceApp filter. Note also that up to date, GAN-based tools are used not only for the seemingly simple purpose of enlarging an image. They can also help generate new images based on the existing input data. For instance, you can implement face aging based on real user photos similar to the renowned FaceApp filter.

How Does GAN Operate?

To put the main principle of GAN operation in simple words. The generative algorithm provides potential samples of the end image elements while the discriminative algorithm defines the correct and incorrect ones among, them based on the processing end requirements.

With every other iteration, several end results may be generated, each of which is juxtaposed with all output parameters. According to that, a “perfect”, most realistic option is selected. As such, two objectives are achieved: the generative algorithm tries to define a maximum number of data samples’ non-correspondences with end requirements, rendering out as many samples as possible; the discriminative algorithm, in turn, thoroughly compares the generated options with the requirements.

Usually, the generative algorithm starts by offering absolutely random samples that gradually, with every other discriminative “investigation”, become more fitting to what the user expects to get as a result. I.e., the “dialogue” between the two algorithms is taking place until the one-one correspondence between the image end parameters and the generated image itself is achieved.    

Example

For a more particular example, we can consider how the Nvidia Hyperrealistic Face Generator algorithm works. It generates new, non-existing in reality faces the following way:

1. A certain collection of live human faces is employed for input parameters;
2. The software’s generative algorithm customizes certain parameters, like the color and length of hair; the form of the ears, eyes, nose, chin; width of the forehead, etc.;
3. The discriminator monitors all the changes. Making sure that everything is implemented correctly (e.g., the eyes must be proportional to the nose, forehead, brow ridges, etc.); 
4. Finally, a user receives the most adequate sample.

When it comes to deep learning-based image scaling, the principles are similar. However, tools for scaling pics without losing quality use particular examples that require processing as source data instead of random images. In turn, such tools set proportions between the source file in the original scale and in the scale that’s minimized a couple of times. 

…More Vivid Cases of GAN Implementation

To solidify your newly-acquired deep learning knowledge. Let’s take a look at several great examples of implementing GAN in the right, efficient way. 

• Prisma. Prisma is a renowned mobile image editor that allows processing regular photos in such a way that they come to resemble handcrafted masterpieces. The app offers dozens of filters inherent to the GAN itself. It was introduced only in 2016, but Prisma has already been deemed the “Application of the year” by Apple. 
• FaceApp. Probably most everyone knows about this one. LIterally a few months ago, Instagram became a domain of the viral photo-aging trend. All that thanks to one of the FaceApp filters that can age people’s faces with utter detalization and quality. Thus, the employment of GAN brought FaceApp developers millions of dollars in the first 10 days of its release.  
• DeepNude. This one used to be quite a controversial solution that visualized what people had under their clothes. When you uploaded a photo to this editor, you could basically see their body without any ethical restrictions, such are the capabilities of GAN. The project was shut down due to a negative public resonance and loads of critique, still, however. Having served as a vivid GAN employment example.  
• Image Upscaler. Yet another prominent GAN-powered solution. If you’re asking yourself right now – “how do I enlarge pictures without losing all the precious quality?” – this is definitely your choice. Image Upscaler is a freeware service that automatically scales images up to 4 times. As a result, you get a high-res picture with all the small details. A more than decent alternative to complex Photoshop and GIMP when it comes to scaling. 

Conclusion

GAN is one of the most progressive deep machine learning solutions out there. It already allows developers to achieve maximum image processing precision. If you want to experience the benefits of this tech concept yourself, welcome to our service that’ll help you enlarge photos without pixelation in the most convenient, accessible way.