Neural Enhance

Neural Enhance is a deep learning image upscaler and restoration tool designed to increase image resolution and remove blur. It functions as a neural image restoration utility for eliminating noise and JPEG artifacts, and includes a framework for training and tuning custom neural network models against image datasets.

The system utilizes a containerized environment to offload tensor calculations to GPU cores, speeding up neural network inference. It features a batch processing pipeline that queues multiple image files in sequence to maximize hardware throughput.

Capabilities include domain-specific upscaling, specialized facial detail enhancement for human portraits, and image artifact removal. The tool supports both the use of pre-trained models and the creation of custom models using perceptual loss and adversarial setups.

Features

Deep Learning Upscalers - Provides deep learning utilities that transform small images into larger versions by estimating original high-resolution details.
Image Restorers - Implements neural network architectures specifically designed to remove noise and JPEG artifacts for image restoration.
CUDA Accelerated Neural Networks - Utilizes CUDA-accelerated neural networks to offload tensor calculations to GPU cores for faster inference and training.
GPU Acceleration - Offloads heavy tensor calculations to GPU cores to significantly reduce neural network processing time during image restoration.

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alexjcneural-enhanceArchived

Features