The Starting Point: Optoelectronics, Not Pure Software
My technical background started from optoelectronic information science and engineering. That background does not make me a traditional optical designer by default, but it gives me a useful way of looking at visual AI systems: images are not just arrays of pixels. They are produced by optics, sensors, sampling, noise, exposure, reconstruction, compression, and deployment constraints.
This is why I am interested in the region between imaging hardware and learning-based perception. A model that works on a clean benchmark can still fail when the sensor pair is misaligned, the visible image is overexposed, the infrared image has low contrast, or the camera pipeline introduces artifacts. I want my work to live closer to those real imaging conditions.
Why I Moved Toward AI-Connected Imaging
I am not trying to build my main identity around traditional optical design, optical materials, or pure medical imaging. Those are important areas, but they are not the direction that best connects my background, interests, and near-term goals.
The direction that feels more durable to me is AI-connected imaging: using learning-based methods while still respecting where visual data comes from. This includes multimodal visual perception, computational imaging, infrared-visible fusion, RGB-T perception, depth cameras, LiDAR or point-cloud perception, ISP-aware restoration, and deployable camera systems.
The common thread is not a single model family. It is a question:
How should AI systems use imperfect visual signals from real sensing pipelines?
That question is broad enough to support future PhD applications in computational imaging, multimodal perception, robotics perception, or computer vision, while also being practical enough for industry roles in ISP, AI camera systems, autonomous sensing, AR/VR, and edge vision.
Multimodal Perception: When One Sensor Is Not Enough
Infrared-visible perception is a natural example. Visible cameras preserve texture, color, and scene detail, but they can be fragile under low light, glare, haze, blur, rain, or overexposure. Infrared sensors can preserve thermal targets and work in darkness, but they may have weaker texture, lower contrast, thermal noise, or less precise boundaries.
The interesting problem is not simply how to merge two images into something visually pleasing. The deeper problem is how to decide which modality should be trusted, where, and under what degradation. In real systems, two modalities are rarely perfectly aligned or equally reliable.
This is the motivation behind my current RobustIRFusion paper line: infrared-centered fusion under misalignment and reliability uncertainty. Instead of treating infrared and visible images as symmetric inputs, I am interested in using infrared as a stable anchor and visible information as conditional detail.
Computational Imaging and AI Camera Restoration
The same thinking also appears in AI camera restoration. Real camera images can contain artifacts caused by optics, sensors, display-camera sampling, demosaicing, denoising, sharpening, compression, or the interaction between these stages.
Photo moire removal is a compact example. When a phone photographs a screen, the display pixel structure interacts with camera sampling and ISP processing. The result is not just generic noise. It is a structured imaging artifact. Removing it well requires both restoration engineering and awareness of how the artifact is formed.
This is why I want to build small portfolio modules under an AI camera restoration line. Each module should be narrow enough to finish, but polished enough to show real engineering taste: reproducible inference, visible before/after results, timing, crop comparisons, failure cases, and clear documentation.
What I Want This Portfolio To Show
This website should not become a heavy third project. It should be a public entry point for the work I am already doing.
The portfolio should show four things:
- I can define research problems from real sensing constraints, not only from benchmark trends.
- I can build reproducible systems around datasets, baselines, metrics, and visualization.
- I can connect optics, ISP, computational imaging, and modern AI methods.
- I can communicate projects clearly enough for future advisors, collaborators, and interviewers to understand the direction quickly.
For now, the site will stay lightweight: project pages, research notes, technical logs, and a small number of polished essays. The goal is not to write constantly. The goal is to make each public note support the larger research narrative.
Current Threads
The current main paper line is RobustIRFusion: misalignment- and reliability-aware infrared-centered fusion for robust multimodal perception.
The current portfolio demo line is Photo Moire Removal: a small AI camera restoration project based first on a reproducible UHDM/ESDNet baseline and a before/after result experience.
Together, these projects form the first version of the identity I want to build:
AI systems for multimodal visual perception, computational imaging, and deployable visual restoration.