Super-resolution image reconstruction is not merely scaling up pixel counts. It involves hallucinating realistic high-frequency textures, removing compression artifacts, and maintaining structural fidelity without blurring sharp edges.
THETAEnhancer+ is an applied computer vision framework designed for practical image restoration and enhancement.
- Computer Vision Projects: https://kholipha-ahmmad-al-amin.me/#projects
- Research and Publications: https://kholipha-ahmmad-al-amin.me/#research
- Engineering Portfolio: https://kholipha-ahmmad-al-amin.me/
Technical Highlights
- Residual Dense Architecture: Employs deep residual blocks that allow high-level feature reuse across multiple spatial resolutions, preventing gradient vanishing during training.
- Perceptual and Adversarial Loss: Rather than relying purely on Mean Squared Error (which produces unnaturally smooth results), we incorporated perceptual loss functions derived from VGG feature layers.
- Inference Optimization: Model weights were quantized and exported to ONNX runtime formats, reducing GPU memory footprints and enabling rapid batch processing.
Discover the full technical specs and demo implementations at https://kholipha-ahmmad-al-amin.me/#projects.
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