Over the years, it has become a necessity to create images from complex measurements using advanced algorithms. The process is called computational imaging, and it is designed to harness computer power to deliver impressive, highly detailed, and accurate imagery. This advanced imaging technology is important because it allows us to overcome hardware limitations.
Why is Computational Imaging So Popular?
The main appeal of computational imaging is that it combines optical image formation with digital image processing. By harnessing computational systems, you can generate an optically encoded image, thanks to optical hardware components. Then, the digitally recorded image is decoded through computational processing. The optical encoding process and digital decoding are linked via a mathematical model.
An interesting aspect of computational imaging is that it has two different branches. First, there is computational photography, which uses digital computation to capture and process images.
Second, we have computer vision, which focuses on creating digital systems that can analyze and interpret data in ways similar to human visual systems. Both computer vision and computational photography are effective because they boost image quality and unlock new visual functionality.
How Does Computational Imaging Work?
The primary role of computational imaging is to combine algorithms, digital signal processing, and optics to enhance and manipulate images. Generally speaking, traditional optics have a variety of limitations, such as dynamic range, depth of field, and resolution constraints. The goal of computational imaging is to overcome these challenges. Once you use computational imaging, you can extract more information, perform 3D reconstruction, conduct image fusion, and many other tasks.
The way computational imaging works is by taking existing data or images and using software to create a more complex version. With computational imaging, you have access to software featuring a wide range of tools to enhance and improve images accordingly. Some of the most common techniques include:
- HDR imaging- designed to help capture a wider range of light and dark tones. It blends exposures to enhance the dynamic range.
- Panorama stitching- takes multiple images to create a 360-degree view or a wide-angle image. The algorithms blend and align these images to produce a single, cohesive result.
- Image stacking- uses algorithms to capture data from multiple but slightly different images. That data is then used to remove noise and enhance overall detail.
- Super-resolution- relies on algorithms to increase an image’s detail and resolution beyond the initial capture. It is designed to take a low-resolution image and upscale it. The same technology can be used to improve detail levels, even when zooming into an image.
- Automatic scene detection- relies on algorithms to detect a scene. Based on its findings, the device can apply specialized optimizations like adjusting color balance, exposure, and other relevant settings.
- Image deblurring- reduces issues like camera shake or motion blur. Image deblurring is common in image processing: it relies on algorithms to analyze movement during capture and then uses software to remove unwanted blur.
Does Generative AI Influence Computational Imaging?
Absolutely. AI offers new ways to detect, implement, and enhance imaging features. Data and images can be manipulate and synthesize using technologies like image synthesis, style transfer, and super-resolution, all of which can be partially or fully automated. This adds a wide array of benefits that enhance the computational imaging workflow.
What Industries Use Computational Imaging?
One of the main benefits of computational imaging is its versatility. It’s especially useful for seismic inversion, computed tomography, and synthetic aperture radar, all of which create 2D or 3D images of landscapes and objects. In addition, computational imaging can be applied in surveillance, retail, sports, transportation, agriculture, smart manufacturing, auto accidents, and even consumer electronics.
Closing Thoughts
Using computational imaging and enhancing its power with AI is highly beneficial. It unlocks a wide range of possibilities and helps deliver more accurate results while reducing uncertainties.
Computational imaging can be used in various scenarios, from enhancing regular images to mixed reality (MR) and augmented reality (AR). As digital imaging processing becomes more advanced, users gain greater power to shape and improve visual content. Moreover, AI integration makes computational imaging even more useful by automating many processes and delivering exceptional visual quality across a myriad of use cases!