How Algorithmic Insight and Scalable Architecture Turn Noisy SEM Images into Reliable Data
Working with SEM images means accepting noise as a given.
Noise is not just a visual artifact - it directly affects measurements, downstream analysis, and scientific conclusions.
This work was carried out as part of an intensive Applied Materials & Extra-Tech bootcamp, where the challenge went far beyond choosing the “right” denoising model.
I would like to thank my mentors Roman Kris and Mor Baram from Applied Materials for their technical guidance, critical questions, and constant push toward practical, production-level thinking, as well as Shmuel Fine and Sara Shimon from Extra-Tech for their support and teaching throughout the process.
Our goal was not simply to “clean images”, but to build a system that treats noise as an algorithmic challenge and solves it at scale.
This is a story about denoising - and about the infrastructure that makes it reliable.
Who is this for?
This post is written for engineers and researchers working with SEM data, image processing pipelines, or machine learning systems that need to operate at scale.
Noise Is Not One Problem - It’s Many
SEM noise rarely follows a single distribution.
In practice, it often combines:
- Gaussian-like noise
- Texture-dependent artifacts
- Frame-to-frame variability within the same dataset
Classical denoising methods provide a natural starting point:
- Mean / Gaussian filters - effective for uniform noise, but blur fine details
- Median / Bilateral filters - preserve edges, struggle with complex noise
- BM3D / NLM - high-quality results, at the cost of heavy computation and careful tuning
Each method solves part of the problem - and introduces new trade-offs.
When Deep Learning Stops Being a Silver Bullet
Deep learning models such as UNet and DRUNet significantly changed the denoising landscape.
They learn noise patterns directly from data rather than relying on fixed assumptions.
However:
- They require high-quality training data
- They are computationally expensive
- They are not always optimal for every noise regime
Replacing classical methods entirely was never the goal.
Instead, we aimed to use deep learning exactly where it provides the most value.
The Hybrid Pipeline: Let Each Method Do What It Does Best
The pipeline was designed as a sequence of informed decisions:
- Classical filtering to stabilize and reduce uniform noise
- Deep learning models to handle complex, non-linear noise patterns
- Quality metrics at every stage to evaluate edges, texture, and detail preservation
Rather than producing a single blind output, the system selects the best result based on measurable criteria.
At this point, a new challenge emerged:
How does this pipeline behave at scale?
Algorithms Don’t Scale - Systems Do
To support large datasets and multiple users, the algorithm needed a solid architectural backbone:
- Multi-client / multi-server design
- Worker pools executing pipeline stages in parallel
- External object storage (S3 / MinIO) for intermediate results
- Redis-based caching to reduce I/O overhead
- Relational database for job state, metrics, and recovery
The denoising logic remained the same -
but performance, stability, and throughput changed dramatically.
Parallelism Done Right
Not all parallelism is equal. The system exploits parallelism across:
- Images - maximizing throughput on large datasets
- Pipeline stages - overlapping CPU- and GPU-heavy tasks
- Execution models - threads for native/CUDA workloads, processes to bypass Python’s GIL
This results in predictable runtimes and efficient resource utilization.
Measure First. Optimize Later.
Before optimizing anything, we benchmarked:
- Per-image latency
- Overall throughput
- CPU and GPU utilization
- I/O overhead
- Impact of concurrent users
Bottlenecks often live outside the model.
Why Caching Changed Everything
Intermediate results are cached using structured keys:
<image_id>:<version>:<stage>:<config_hash>
This enables:
- Instant reuse of previous computations
- True stop-and-resume capabilities
- Cross-server result sharing
In practice, this eliminated hours of redundant processing.
Key Takeaways
- A strong algorithm needs a system that supports it
- Hybrid approaches outperform single-method solutions
- Metrics are part of the algorithm, not an afterthought
- Caching and parallelism are force multipliers
- Good architecture allows algorithms to shine
Final Thoughts
The core of SEM denoising lies in algorithms that maximize measurable quality metrics while minimizing information loss.
By combining algorithmic insight, deep learning, and scalable architecture, noisy SEM images become reliable data.
That is the journey from Pixels to Precision.
Closing Thought:
If you’re facing similar challenges in image processing or machine learning systems,
I’d be happy to hear how you approach noise and scalability in your own pipelines.
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