Accelerate Image Processing Tasks With Nvidia GPUs

Graphic Processing Units (GPUs) have crossed new horizons and demonstrated notable advantages in Big Data processing and other computation-intensive operations. Apart from their most widely known application in rendering 2D/3D graphics and animations in the gaming industry, modern GPUs can also be coded to perform complex computations such as image processing for scientific simulations and architecture projects, blockchain/cryptocurrency development, and mining, Data Analytics, AI/ML development, and handling sophisticated frameworks like OpenCL, CUDA, etc. 

The global GPU market is projected to grow to almost 300 billion USD by 2027. GPU adoption across industries will continue to soar exponentially as AI/ML-assisted analytics systems and chatbots proliferate, cryptocurrencies, NFTs, and digital markets grow in scale and utility, and AI-assisted fraud and risk assessment become more commonplace.

Today, many AI-based analytics, insurance assessment, and fraud prevention applications rely on images as input data. GPU’s inbuilt prowess in processing and manipulating images is hence no longer limited to the gaming and cinematic industries. Still, it is also irreversibly enmeshed with the healthcare sector, financial services industry, judicial and reformation systems, and biometrics and identity management technologies, among others. The advanced mathematical algorithms that constitute the bedrock of such complex image-processing systems require deft handling, which is child’s play for today's state-of-the-art GPUs. 

This article will give a comprehensive guide on the intricacies of image processing and how Nvidia GPUs accelerate image processing tasks. 

What Is Image Processing?

Image processing is the concept of manipulating an existing digital image and transforming it into a different image or an alternative digital format altogether through algorithms and mathematical functions. This can be as simple as transforming a colored image to a grayscale one or as complex as accurately and automatically identifying an individual or a numberplate from a series of frames taken from a CCTV video. 

The image processing technology is no longer limited to QR/barcode/OCR recognition and biometric identification. Still, it is rapidly emerging as a key methodology for AI engineers and Data analysts across industries to extract meaningful insights from images. Image processing techniques are now the fastest-growing technologies employed in sectors such as AI/ML, healthcare, traffic management, crime and criminal identification, fraud detection, etc. Graphics design, 3D-image rendering, architectural simulations, protein/vaccine development visualization, etc., are other image processing use cases. The global image recognition market itself is forecasted to grow to USD 53 billion by 2025. Imagine the size of the image-recognition applications and products market! 

The general steps involved in image processing algorithms are as follows:  

1. Image acquisition: The first step involves retrieving a pre-existing digital image file either from a hardware resource or from another input source. This stage also involves storing and displaying the retrieved image for the next image-processing phases. 
2. Image enhancement: This phase involves enhancing the digital image and converting it to a format suitable for analysis. The simplest example of this is sharpening or brightening an image to identify an individual, object, or element present in it.  
3. Image restoration: Deploying algorithms to process a noisy, blurry, or less reasonable image to extract maximum information from it. This phase often involves the Point Spread Function (PSF) to restore the lost information. This has applications in astronomy, microscopy, etc. 
4. Color processing: Color plays a significant role in image processing and can also be used as a key descriptor for identifying objects and extracting information. For this, the algorithms need to recognize the physics of light and shadow associated with color.  
5. Wavelet processing: The wavelet transform technique is a powerful tool to extract information and reduce noise in an image by capturing both the frequency and the location information of individual sections of a digital image. This also has an application in image compression. 
6. Segmentation: Dividing an image into smaller sections depending on several visual criteria and processing these segments separately. This is regarded as the most difficult component of image processing.  
7. Image compression: Some image processing methods also use image compression to reduce the image size for accelerated extraction of information. For generic data processing, such as identifying dogs, cats, or other objects without going into the details, image compression helps reduce unnecessary clutter surrounding the relevant objects. Image compression is also handy for long-term storage and retrieval. 
8. Recognition: Recognizing objects in an image and assigning appropriate labels using pre-trained AI/ML software. 
9. Character recognition: An important component of most modern image processing algorithms, this has applications in OCR/OMR technologies, digitizing handwritten/historical documents, legal/property documentation, etc. This involves identifying and extracting written words/ characters in a scanned image as valuable information.

Considerations for Image Processing Operations 

Several factors contribute to achieving fast image processing and object recognition. Let's understand some of them: 

• Parallelization potential: Image processing tasks can meaningfully benefit from parallel processing as a pixel does not rely on information from its neighboring pixels for details regarding color, shading, illumination, vectoring, etc. This is the reason that GPUs with hundreds (or thousands!) of parallel processing cores excel at handling image-processing algorithms. Parallelization can significantly accelerate image processing and reduce the overall time required. 
• Latency: CPUs provide parallel processing on a modest scale (in terms of pixels, image frames, etc.), leading to latency in the overall image retrieval, display, and editing. GPU-accelerated systems are renowned for undertaking advanced computation applications, especially graphics processing (as the name suggests!), with minimal latency. 
• Pixel locality: An important criterion when dealing with image processing is the positioning or localization of individual pixels. Put simply, each pixel is assumed to be affected by its neighboring pixel in terms of relative coloration, shading, illumination, etc. This aspect comes into play during advanced image processing, such as sharpening, blurring, applying filters, panorama stitching, etc. The speed of image processing thus depends on the number of pixels (i.e., resolution) and the positions of a finite number of neighboring pixels to be referenced. 
• Multi-level optimization: Image processing inherently runs multiple algorithms that perform at different intensities and rates. For efficient rendering, these algorithms must be supported by hardware, coding, and algorithmic optimizations at each level to reduce wait times and unnecessary lags.  

Why Nvidia GPUs for Image Processing?  

Some notable reasons why Nvidia GPUs are most appropriate for complex image processing:

• High speed: Nvidia GPUs have been demonstrated to perform hundreds of times faster than CPUs across benchmarks. They are reliable workhorses when it comes to accelerating heavy image-based operations. As on-chip memory and processing bandwidth increases across generations, these GPUs can seamlessly perform increasingly more convoluted pixel processing and image rendering.  
• Better visuals: High-end Nvidia GPUs like the A100 are equipped with the revolutionary Ray Tracing suite (RT), which can be used to generate highly realistic graphics and complex large-scale models. Exceedingly popular in the gaming and film production industries, this is rapidly gaining a toehold in architectural visualization, scientific visualization, product design, etc. Nvidia GPUs also feature super-sampling and denoising technologies that output high-resolution visuals faster.  
• Embedded libraries and applications: Nvidia is known for collaborating with Opensource application developers as well as supporting Python and AI/ML libraries like CUDA, OpenCV, NVIDIA Optical Flow SDK, Pillow/PIL, nvJPEG, Video Codec SDK, SimpleITK, etc., deliver dramatically higher image processing performance. Powerful Python image-processing libraries that require high computational power include Scikit-image, Mahotas, Matplotlib, OpenCV, SciPy, etc., which can also leverage GPU acceleration. 
• SIMT architecture: GPUs are best suited for sophisticated image processing algorithms and video/image object identification. This is because they utilize Single Instruction Multiple Threads architecture (SIMT) in which (at least) 32 threads simultaneously subject different datasets to the same instruction. This is highly advantageous for image processing, where millions of pixels must be individually developed in line with the same instruction set.  
• Shared memory: All the latest GPUs, especially the ones from Nvidia, are manufactured with shared memory. This type of memory is allocated at the block level. It is accessible by all the threads of that block, allowing threads to cooperate efficiently by synchronizing read-and-write operations. Such synchronization makes image processing much swifter compared to other memory types and caching techniques.  
• Augmented image quality: Nvidia is reputed globally for its image and video rendering innovations and has come up with various stunning image processing technologies over the years. Ray tracing is an advanced tool that employs proper simulation of the physical behavior of light for realistic image development and display. Again, Nvidia GPUs excel at creating high-res immersive images because of DLSS technology, which uses Neural network algorithms. 
• Support for Developers: Nvidia has an entire library of resources, ebooks, knowledgebase, and tutorials dedicated to helping developers and content creators hone their image-processing skills and churn out our outstanding visual media and image-as-input applications.  

Conclusion

We hope this article provides a comprehensive understanding of the fundamentals of image processing and the criteria to be considered when deciding to switch from CPU to GPU for developing image processing/object recognition applications. We also had a cursory glance at some of the key features of Nvidia GPUs that highlight them as optimal for image processing operations.