In today's world, image analysis is omnipresent in all technology. From facial recognition to medical diagnostics to autonomous vehicles, all use this type of analysis in some manner. Deep Learning Convolutional Neural Networks are essentially layers of algorithms that process images. They are used by computers and have helped in revolutionizing computer vision.
However, the problem that arises with CNNs (Convolutional Neutral Networks) is that they classify images using prior-trained data, which sometimes results in memorizing or developing stereotypes. They are also defenceless against adversarial attacks that cause small, nearly – imperceptible distortions in the image leading to bad decisions. These drawbacks, along with the excessive carbon footprint associated with CNNs, have limited its usefulness considerably.
Combining conventional computer vision with optical pre-processors may be one way to improve the reliability and energy efficiency of image processing algorithms. This type of hybrid system can work with nominal electronic hardware. They save time and energy as light completes mathematical functions without dissipating energy in the pre-processing stage. This emerging approach might overcome the shortcomings of deep learning and further exploit the advantages of both optics and electronics, causing Computer Vision Market to develop.
Recent research demonstrated the viability of hybrid computer vision systems with the use of the application of optical vortices. Vortices are swirling waves of light with a dark central spot and can be equated with hydrodynamic whirlpools, which are created when light travels around corners and edges.
Knowledge of vortices can also be used to understand random wave patterns. When imprinted with whirlpools, optical image data travels in a way that highlights and mixes different parts of visual images. The researchers have hinted that vortex images, pre-processing with shallow, small-brain neutral networks may function in place of CNNs.
Optical vortices are equipped with a distinctive advantage, which is their mathematical, edge-enhancing function. The research showed that the optical vortex encoder produces object intensity data in a way that helps a small brain neural network to rapidly reconstruct the primary image from its optically pre-processed pattern.
The demonstration showed that optical pre-processing might prevent the use of CNNs and turn out to be more robust than them. Unlike CNN's, optical pre-processing has the capacity to generalize solutions for inverse problems. For example, when a hybrid neural network learns the shape of handwritten digits, it can consequently reconstruct Japanese or Arabic characters, even those that it has not seen before.
The paper also indicates that reducing any image into fewer, high-intensity pixels is capable of exceptionally low-light image processing. The research brings new insight into photonics' role in developing real-time hardware for big data analytics and building generalizable small-brain hybrid neural networks.
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