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Felldude/
Large_EdgeAware_410_MAE

custom-dataset-8bit-to-16bit

cc-by-nc-nd-4.0

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16bit-from-8bit Image Reconstruction Model

This model reconstructs 16-bit per channel images from standard 8-bit input images. It is trained on paired datasets and optimized to preserve color fidelity, structural consistency, and high-frequency detail.

Median MAE: 410
LPIPS (Alex): ~0.044 (60-image evaluation)
Architecture Update: Added Leaky ReLU
Training Resolution: 256×256(46k Patches from Raw HDR images with 8,580 48bit synthetic images.)
Training Resolution: 512×512 (Hand Selected Dataset 2k)
Training Resolution: 1024×1024 (Hand Selected Dataset 500)

Dataset

Total images: 54,580
- RAW patch images: 46,000 @ 256x256 (~10 GB)
- 48-bit synthetic images: 8,580 (~2 GB)

Addtional 10GB in Hand Selected RAW images, for the 512px and 1024px High Frequency Training Passes

MAE Distribution (8-bit → 16-bit reconstruction)

MAE Range	Accuracy Comment	Percent (%)
≥1000	Occasionally visible in uniform areas	1.06
600–1000	Almost never visible	10.03
400–600	Fully imperceptible	27.39
200–400	Near perfect	59.95
≤200	Near exact scientific	1.57

Perceptual Metrics (60-image test set)

Metric	Result	Interpretation
LPIPS (Alex)	0.044	Low perceptual distance / high similarity
Gradient Energy	0.088 → 0.108	Preserved fine detail, slight sharpening
FFT Structure Score	1.07 → 1.23	Improved high-frequency retention
Histogram Continuity	11.2 → 11.3	Stable tonal distribution

Interpretation Summary

LPIPS values (~0.03–0.07 range) indicate high perceptual similarity
Structural metrics (FFT + gradients) show consistent detail reconstruction
Histogram stability indicates no major tonal drift between bit-depth conversions

Intended Use

Primary Use Cases

Reconstruction of 16-bit per channel images from 8-bit input
JPG & GIF post-processing and enhancement
Archival and art restoration workflows

Not Intended For

Lossless scientific measurement or precision tasks
Medical AI enhancement

Felldude/Large_EdgeAware_410_MAE