# alexjc/neural-enhance **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/alexjc-neural-enhance).** 11,873 stars · 1,361 forks · Python · AGPL-3.0 · archived ## Links - GitHub: https://github.com/alexjc/neural-enhance - awesome-repositories: https://awesome-repositories.com/repository/alexjc-neural-enhance.md ## Description 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. ## Tags ### Business & Productivity Software - [Deep Learning Upscalers](https://awesome-repositories.com/f/business-productivity-software/desktop-application-enhancers/resolution-upscalers/deep-learning-upscalers.md) — Provides deep learning utilities that transform small images into larger versions by estimating original high-resolution details. ### Artificial Intelligence & ML - [Image Restorers](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-neural-networks/image-restorers.md) — Implements neural network architectures specifically designed to remove noise and JPEG artifacts for image restoration. - [CUDA Accelerated Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/cuda-accelerated-neural-networks.md) — Utilizes CUDA-accelerated neural networks to offload tensor calculations to GPU cores for faster inference and training. - [GPU Acceleration](https://awesome-repositories.com/f/artificial-intelligence-ml/gpu-acceleration.md) — Offloads heavy tensor calculations to GPU cores to significantly reduce neural network processing time during image restoration. - [Hardware-Accelerated Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-accelerated-inference.md) — Utilizes compatible graphics cards to speed up the neural network inference process for high-resolution image upscaling. ([source](https://github.com/alexjc/neural-enhance#readme)) - [Super-Resolution Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/pre-trained-model-application/super-resolution-inference.md) — Uses pre-trained deep learning models to generate high-resolution pixels based on low-resolution input patterns. - [Backpropagation](https://awesome-repositories.com/f/artificial-intelligence-ml/backpropagation.md) — Implements backpropagation algorithms to calculate gradients and update model parameters for super-resolution output optimization. - [Backpropagation Training Loops](https://awesome-repositories.com/f/artificial-intelligence-ml/backpropagation-training-loops.md) — The system adjusts neural network weights through backpropagation using image datasets to optimize resolution output. - [Custom Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training.md) — Provides a framework for training custom neural networks on specific image datasets using perceptual loss and adversarial setups. ([source](https://github.com/alexjc/neural-enhance#readme)) - [Neural Network Trainers](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-networks/neural-network-trainers.md) — Provides a framework for training and tuning custom neural network models against image datasets. ### Graphics & Multimedia - [GPU Hardware Acceleration](https://awesome-repositories.com/f/graphics-multimedia/gpu-hardware-acceleration.md) — Interfaces with graphics processing units to enable high-performance compute for heavy neural network tasks. - [Neural Artifact Removal](https://awesome-repositories.com/f/graphics-multimedia/neural-artifact-removal.md) — Removes JPEG artifacts, noise, and blur from images using neural networks while preserving original dimensions. ([source](https://github.com/alexjc/neural-enhance#readme)) - [Neural Image Processors](https://awesome-repositories.com/f/graphics-multimedia/neural-image-processors.md) — Provides a neural image processor that applies deep learning inference for advanced image manipulation. - [Image Processing Pipelines](https://awesome-repositories.com/f/graphics-multimedia/image-processing-pipelines.md) — Implements a sequential workflow for queuing and processing multiple image files to maximize throughput. - [Image-to-Tensor Conversions](https://awesome-repositories.com/f/graphics-multimedia/image-to-tensor-conversions.md) — Transforms raster image pixels into high-dimensional tensors for processing through neural network layers. - [Detail Enhancement](https://awesome-repositories.com/f/graphics-multimedia/media-processing-analysis/face-portrait-manipulation/face-manipulation/detail-enhancement.md) — Applies specialized super-resolution models to sharpen features and restore high-definition details in human portraits. ([source](https://github.com/alexjc/neural-enhance#readme)) ### Part of an Awesome List - [Super-Resolution Domain-Specific Models](https://awesome-repositories.com/f/awesome-lists/ai/pre-trained-models/domain-specific-pre-training/super-resolution-domain-specific-models.md) — Utilizes specialized neural networks trained on specific image categories to produce higher fidelity upscaling results. ([source](https://github.com/alexjc/neural-enhance#readme)) ### Data & Databases - [Image Processing Batchers](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/batch-processing-systems/batch-processing-utilities/image-processing-batchers.md) — Includes utilities for automating bulk image manipulation tasks through a sequential processing pipeline. ### Development Tools & Productivity - [Batch Processing Pipelines](https://awesome-repositories.com/f/development-tools-productivity/batch-processing-pipelines.md) — Provides an automated workflow for sequentially processing image files to maximize hardware throughput.