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# Title Team Members TA Documents Sponsor
23 Portable RAW Reconstruction Accelerator for Legacy CCD Imaging
 Arnav Gaddam
Guyan Wang
Yuhong Chen
 Gerasimos Gerogiannis design_document1.pdf
other1.docx
other2.pdf
# **RFA: Portable RAW Reconstruction Accelerator for Legacy CCD Imaging**

Group Member: Guyan Wang, Yuhong Chen

## **1\. Problem Statement**

**The "Glass-Silicon Gap":** Many legacy digital cameras (circa 2000-2010) are equipped with premium optics (Leica, Zeiss, high-grade Nikon/Canon glass) that outresolve their internal processing pipelines. While the optical pathway is high-fidelity, the final image quality is bottlenecked by:

- **Obsolete Signal Chains:** Early-stage Analogue-to-Digital Converters (ADCs) and readout circuits introduce significant read noise and pattern noise.
- **Destructive Processing:** In-camera JPEGs destroy dynamic range and detail. Even legacy RAW files are often processed with rudimentary demosaicing algorithms that fail to distinguish high-frequency texture from sensor noise.
- **Usability Void:** Users seeking the unique "CCD look" are forced to rely on cumbersome desktop post-processing workflows (e.g., Lightroom, Topaz), preventing a portable, shoot-to-share experience.

## **2\. Solution Overview**

**The "Digital Back" External Accelerator:** We propose a standalone, handheld hardware device-a "smart reconstruction box"-that interfaces physically with legacy CCD cameras. Instead of relying on the camera's internal image processor, this device ingests the raw sensor data (CCD RAW) and applies a hybrid reconstruction pipeline.

The core innovation is a **Hardware-Oriented Hybrid Pipeline**:

- **Classical Signal Processing:** Handles deterministic error correction (black level subtraction, gain normalization, hot pixel mapping).
- **Learned Estimator (AI):** A lightweight Convolutional Neural Network (CNN) or Vision Transformer model optimized for microcontroller inference (TinyML). This model does not "hallucinate" new details but acts as a probabilistic estimator to separate signal from stochastic noise based on the physics of CCD sensor characteristics.

The device will feature a touchscreen interface for file selection and "film simulation" style filter application, targeting an output quality perceptually comparable to a modern full-frame sensor (e.g., Sony A7 III) in terms of dynamic range recovery and noise floor.

## **3\. Solution Components**

### **Component A: The Compute Core (Embedded Host)**

- **MCU:** STMicroelectronics **STM32H7 Series** (e.g., STM32H747/H757).
- _Rationale:_ Dual-core architecture (Cortex-M7 + M4) allows separation of UI logic and heavy DSP operations. The Chrom-ART Accelerator helps with display handling, while the high clock speed supports the computationally intensive reconstruction algorithms.
- **Memory:** External SDRAM/HyperRAM expansion (essential for buffering full-resolution RAW files, e.g., 10MP-24MP) and high-speed QSPI Flash for AI model weight storage.

### **Component B: Connectivity & Data Ingestion Interface**

- **Physical I/O:** USB OTG (On-The-Go) Host port.
- _Function:_ The device acts as a USB Host, mounting the camera (or the camera's card reader) as a Mass Storage Device to pull RAW files (.CR2, .NEF, .RAF, .DNG).
- **Storage:** On-board MicroSD card slot for saving processed/reconstructed JPEGs or TIFFs.

### **Component C: Hybrid Reconstruction Algorithm**

- **Stage 1 (DSP):** Linearization, dark frame subtraction (optional calibration), and white balance gain application.
- **Stage 2 (NPU/AI):** A quantization-aware trained model (likely TFLite for Microcontrollers or STM32-AI) trained specifically on _noisy CCD -to- clean CMOS_ image pairs.
- _Task:_ Joint Demosaicing and Denoising (JDD).
- **Stage 3 (Color):** Application of specific "Film Looks" (LUTs) selected by the user via the UI.

### **Component D: Human-Machine Interface (HMI)**

- **Display:** 2.8" to 3.5" Capacitive Touchscreen (SPI or MIPI DSI interface).
- **GUI Stack:** TouchGFX or LVGL.
- _Workflow:_ User plugs in camera -> Device scans for RAWs -> User selects thumbnails -> User chooses "Filter/Profile" -> Device processes and saves to SD card.

## **4\. Criterion for Success**

To be considered successful, the prototype must meet the following benchmarks:

- **Quality Parity:** The output image, when blind-tested against the same scene shot on a modern CMOS sensor (Sony A7 III class), must show statistically insignificant differences in perceived noise at ISO 400-800 equivalent.
- **Edge Preservation:** The AI reconstruction must demonstrate a reduction in color moiré and false-color artifacts compared to standard bilinear demosaicing, without "smoothing" genuine texture (measured via MTF charts).
- **Latency:** Total processing time for a 10-megapixel RAW file must be under **15 seconds** on the STM32 hardware.
- **Universal RAW Support:** Successful parsing and decoding of at least two major legacy formats (e.g., Nikon .NEF from D200 era and Canon .CR2 from 5D Classic era).

## **5\. Alternatives**

- **Desktop Post-Processing (Software Only):**
- _Pros:_ Infinite computing power, established tools (DxO PureRAW), highly customized.
- _Cons:_ Destroys the portability of the photography experience; cannot be done "in the field." Need to be proficient with parameters inside the software, which requires self-training and tutoring (not user-friendly).
- **Smartphone App (via USB-C dongle):**
- _Pros:_ Powerful processors (Snapdragon/A-Series), high-res screens, easy to use.
- _Cons:_ Lack of low-level control over USB mass storage protocols for obscure legacy cameras; high friction in file management; operating system overhead prevents bare-metal optimization of the signal pipeline; unique algorithms may not be suitable for legacy cameras.
- **FPGA Implementation (Zynq/Cyclone):**
- _Pros:_ Parallel processing could make reconstruction instant.
- _Cons:_ Significantly higher complexity, cost, and power consumption compared to an STM32 implementation; higher barrier to entry for a "mini project."

El Durazno Wind Turbine Project

Alexander Hardiek, Saanil Joshi, Ganpath Karl

El Durazno Wind Turbine Project

Featured Project

Partners: Alexander Hardiek (ahardi6), Saanil Joshi (stjoshi2), and Ganpath Karl (gkarl2)

Project Description: We have decided to innovate a low cost wind turbine to help the villagers of El Durazno in Guatemala access water from mountains, based on the pitch of Prof. Ann Witmer.

Problem: There is currently no water distribution system in place for the villagers to gain access to water. They have to travel my foot over larger distances on mountainous terrain to fetch water. For this reason, it would be better if water could be pumped to a containment tank closer to the village and hopefully distributed with the help of a gravity flow system.

There is an electrical grid system present, however, it is too expensive for the villagers to use. Therefore, we need a cheap renewable energy solution to the problem. Solar energy is not possible as the mountain does not receive enough solar energy to power a motor. Wind energy is a good alternative as the wind speeds and high and since it is a mountain, there is no hindrance to the wind flow.

Solution Overview: We are solving the power generation challenge created by a mismatch between the speed of the wind and the necessary rotational speed required to produce power by the turbine’s generator. We have access to several used car parts, allowing us to salvage or modify different induction motors and gears to make the system work.

We have two approaches we are taking. One method is converting the induction motor to a generator by removing the need of an initial battery input and using the magnetic field created by the magnets. The other method is to rewire the stator so the motor can spin at the necessary rpm.

Subsystems: Our system components are split into two categories: Mechanical and Electrical. All mechanical components came from a used Toyota car such as the wheel hub cap, serpentine belt, car body blade, wheel hub, torsion rod. These components help us covert wind energy into mechanical energy and are already built and ready. Meanwhile, the electrical components are available in the car such as the alternator (induction motor) and are designed by us such as the power electronics (AC/DC converters). We will use capacitors, diodes, relays, resistors and integrated circuits on our printed circuit boards to develop the power electronics. Our electrical components convert the mechanical energy in the turbine into electrical energy available to the residents.

Criterion for success: Our project will be successful when we can successfully convert the available wind energy from our meteorological data into electricity at a low cost from reusable parts available to the residents of El Durazno. In the future, their residents will prototype several versions of our turbine to pump water from the mountains.