Optical systems and artificial intelligence

By: Assistant Professor Dr. Safaa Mustafa Hameed
Assistant to the University President for Academic Affairs
The integration of optical systems and artificial intelligence today is one of the most vital paths in scientific research, as we have transitioned from the camera as a tool that "captures" the world to a comprehensive system that "understands" and reshapes it. In traditional optical systems, lenses, mirrors, and filters define the limits of what can be measured in terms of accuracy, dynamic range, weight, size, and cost. However, these physical limits, including the effects of diffraction, noise, and manufacturing imperfections, are no longer strict constraints after the advent of computational imaging and deep learning. Here, artificial intelligence appears as a reasoning layer that reconstructs the scene from incomplete or distorted measurements, or as part of a joint design where the engineer coordinates between what the optics do in the front and what the algorithm handles in the back, so that the hardware serves the algorithmic goal from the very first moment the signal is formed.
From a mathematical perspective, the linear imaging process can be approximated by considering the measured image as a convolution of the real scene with the point spread function, in addition to distortions. The problem is formulated either as a constrained optimization with a statistical priority that organizes the solution, or it is replaced by a deep learning map that learns to transform the measurements into a reliable reconstruction. In recent years, research has focused on incorporating physics itself into the core of the learning model through what is known as optimization unraveling in the form of trainable layers, which grants the algorithm higher interpretability and better stability when capture conditions change.
This convergence between optics and algorithms does not happen in a vacuum; it is based on a parallel revolution in systems. The emergence of meta-surfaces and super-elements has enabled the control of phase, polarization, and wavelength thru thin nanostructures, making it possible to manufacture lightweight "flat lenses" that perform complex functions unattainable with thick glass lenses alone. Unconventional sensors have also emerged, such as event cameras that encode temporal changes instead of sequential frames, and single-photon detectors that allow operation at extremely low light levels, in addition to integrated spectral and polarization arrays. This coincided with photonic-electronic integration, enabling linear transformations at a much lower energy cost than their purely electronic counterparts, paving the way for a wide range of portable and edge applications.
At the application level, computational imaging has changed the quality equation in low-light conditions or fast motion; it has become possible to remove blur, compensate for noise, and restore details without resorting to massive optics or long exposure times. In spectral and polarimetric imaging, compressed measurements—where the optical mask is programmed to record an intelligent mix of information—provide a retrievable spectral cube with a learning algorithm, extracting valuable chemical or structural insights in medicine, non-destructive testing, and environmental sensing. Autonomous vehicles benefit from this leap thru computer vision that operates in real-time on 3D point flows, where noise is filtered out and structural features that can be used for decision-making are extracted with higher speed and safety.
In bio-microscopy, the integration of structured illumination and deep learning represented a qualitative leap; the illumination pattern is designed to reveal information that the network can easily reconstruct with a lower photon dose, preserving sample integrity and doubling spatial and temporal resolution. This logic extends to the design of the systems themselves, where the specifications of optical components—from phase masks to meta-surface engineering—are treated as learnable parameters within a unified training pathway. The ultimate goal is not only "image sharpness" but also the accuracy of the desired task, such as classification, depth estimation, or spectral identification. In this way, the point spread function becomes "task-oriented," meaning it is encoded from the beginning in a way that helps the algorithm later to resolve ambiguities.
The scene advances further with photonic neural networks that exploit interference and diffraction to perform matrix multiplications on light itself. These photonic processors promise immense speed and high energy efficiency, yet they face practical challenges such as thermal drift, tuning precision, and noise calibration. However, the combination of electronic tuning and reconfigurable photonic components opens the door to hybrid modes that perform intensive optical operations while maintaining flexibility in learning and adaptation.
Despite this momentum, reliability and generalization remain central challenges. Models trained under specific lighting, spectral, or temperature conditions may deteriorate outside their training range, and data bias can lead to unfair decision-making in sensitive applications such as driving and medicine. Additionally, there is the risk of adversarial attacks that may exploit the system's vulnerability to minor visual changes, as well as the energy and environmental costs associated with training large models, along with the continuous calibration requirements that arise as the properties of optical elements change over time. Therefore, systematic evaluation—using both image quality metrics and final task metrics—and "out-of-distribution" tests, along with lab and field calibration, become an integral part of any serious development line.
The near future is poised for further integration "from lens to learning," where the design issue is framed as a probabilistic optimization that maximizes the information exchanged between the scene and the measurements, and manufacturing constraints are embedded from the outset to ensure that models align with reality, not just simulation. Event-based sensing technologies and time series models will continue to advance, reducing latency and energy consumption. At the same time, photonic neural processors will evolve toward more stable arrays and higher tuning precision. Conversely, models will move toward brevity and efficiency through compression techniques, knowledge transfer, and quantization, making inference possible directly at the optical edge without always needing the cloud.
In summary, the marriage between optical systems and artificial intelligence has redefined what a machine can "see" and what it should "understand." We have emerged from the confines of static optics into the realm of purposeful design, which redistributes roles between hardware and algorithms according to the mission's objectives. And between thin multifunctional lenses, unconventional sensors, and physically interpretable deep networks, a new layer of computational imaging is forming, capable of operating in conditions that were classified as "impossible" until recently. However, the path to reliable, fair, and safe systems inevitably passes through more experimental discipline, bias review, and building models that rely on physics as much as they learn from data, so that we fulfill the promises of this field without compromising human safety and decision reliability.