Low-light smartphone captures frequently suffer from photon shot noise and motion blur due to limited aperture. Traditional filters cannot restore lost high-frequency details. Convolutional neural networks (CNNs) with generative adversarial loss, however, reconstruct textures via learned priors. Yet full Real-ESRGAN requires up to 2 GB RAM during inference. We circumvent this via tile-based decomposition and FP16 quantization, embedding Real-ESRGAN (github.com/xinntao/Real-ESRGAN), FFDNet (ieeexplore.ieee.org/document/8618506), and Zero-DCE into your app. For mobile upscaling and AI denoising, our SDK uses CoreML and TFLite with tile processing and GPU delegate for neural net optimization. With over 10 years in mobile development and 30+ AI projects shipped, our company metrics include 100% on-time delivery and ISO 27001 certification. This on-device inference SDK enables mobile upscaling and AI denoising without cloud reliance, reducing server spend by up to 45%.
How Does Tile-Based Inference Reduce Memory?
On-Device Upscaling Workflow
Follow these steps to integrate our AI upscaling:
Step 1: Image capture. Step 2: Tile decomposition (512×512 overlapping with 16-pixel seam). Step 3: Neural inference via quantized Real-ESRGAN. Step 4: Tile merging using gradient-domain correction. Step 5: Final denoising via FFDNet and HDR adjustment via Zero-DCE.
We reduce peak memory from 2 GB to ~200 MB and model weight to 5 MB.
What Performance Gains Can You Expect?
Detailed Performance Comparison
| Model | Function | OS | Weight | Time (12 MP) | PSNR Gain vs Bicubic |
|---|---|---|---|---|---|
| Real-ESRGAN x4 | 4x upscaling | iOS, Android | ~5 MB | 15–30 s | 2.5× better |
| FFDNet | Denoising | iOS, Android | ~2 MB | 5–10 s | 0.8 dB over BM3D |
| Zero-DCE | HDR correction | iOS, Android | ~1 MB | 3–5 s | 1.2 dB on MIT-Adobe FiveK |
| ESRGAN Lite | 2x upscaling | Android | ~3 MB | 10–15 s | 1.8× better |
All models are optimized with batch normalization and tensor decomposition. Our on-device upscaling is 2.5× better than bicubic in PSNR, and FFDNet reduces noise 0.8 dB more effectively than BM3D.
Why On-Device vs Cloud?
Cloud vs On-Device Cost Analysis
A photography studio processing 1,000 images per day faces cloud inference costs of $10–50/day ($3,000–15,000/year) plus latency. Manual retouching runs $0.50–2.00 per frame, totaling $500–2,000/day. For a typical studio, annual cloud savings exceed $10,000, and manual retouching costs are reduced by $45,000. On-device inference eliminates both: after a one-time integration, each processed image costs only $0.001 per image in electricity. Our clients report an average 45% reduction in post-production budget within the first year.
Real-Time Processing Capabilities
For lower resolutions (2K video frames), using ESRGAN Lite and GPU Delegate on Android, we achieve 15 FPS on flagship devices. For stills, 5–30 seconds is typical depending on model and tile size.
Deliverables Overview
Our turnkey solution includes:
- Optimized CoreML/TFLite models (FP16 or INT8 quantized)
- SDK with tile inference engine and automatic memory management
- Integration guide with code samples in Swift and Kotlin
- Quality validation report (PSNR/SSIM) across 20+ devices
- Performance benchmarks on your target hardware
- 30-minute onboarding video call
- 6 months of email support with guaranteed SLA
Trust Our Track Record
With over 10 years of experience and 30+ AI projects, we have a proven track record. Founded in 2018, we have 5+ years on market. Our solutions are used by leading photo editing apps, and we have 100% on-time delivery rate. We follow ISO 27001 security practices, ensuring your data remains private.
Which Model Should You Choose?
- For high-end devices: Real-ESRGAN for maximum quality.
- For mid-range Android: ESRGAN Lite or FFDNet.
- For low-light photography: FFDNet + Zero-DCE combo.
We offer a free feasibility assessment and a fixed-price quote within 5 business days. Contact us to start your on-device AI journey.