# spipm/depixelization_poc

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4,535 stars · 368 forks · Python · NOASSERTION

## Links

- GitHub: https://github.com/spipm/Depixelization_poc
- awesome-repositories: https://awesome-repositories.com/repository/spipm-depixelization-poc.md

## Description

This project is an AI upscaling framework and deep learning image restorer designed to estimate original source pixels from low-resolution inputs. It functions as a super-resolution reconstruction system that transforms pixelated images into high-resolution versions by restoring high-frequency details and sharpening edges.

The system utilizes a convolutional neural network pipeline to analyze pixel data and perform digital image restoration. It employs pixel-shuffle upsampling to rearrange channel dimensions into spatial dimensions, which increases resolution while reducing checkerboard artifacts.

The framework incorporates a tensor-based data pipeline for parallel processing on graphics processing units and uses loss-function optimization to minimize the difference between reconstructed pixels and high-resolution ground truth.

## Tags

### Part of an Awesome List

- [Super Resolution](https://awesome-repositories.com/f/awesome-lists/ai/super-resolution.md) — Provides a super-resolution framework that transforms low-resolution inputs into high-resolution versions by estimating missing pixels.
- [Image Reconstruction](https://awesome-repositories.com/f/awesome-lists/ai/image-reconstruction.md) — Uses transformer-based reconstruction techniques to restore high-resolution details to pixelated images.

### Artificial Intelligence & ML

- [Convolutional Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-neural-networks.md) — Implements a convolutional neural network pipeline to analyze pixel data and recover high-frequency image details.
- [Image Restorers](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-neural-networks/image-restorers.md) — Recovers lost detail from degraded or compressed images to make them appear sharper and more natural.
- [Deep Learning Image Processors](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-image-processors.md) — Utilizes convolutional neural networks and tensor-based pipelines to analyze and modify pixel data for enhancement.
- [Image Restoration Models](https://awesome-repositories.com/f/artificial-intelligence-ml/image-restoration-models.md) — Implements a reconstruction system that estimates missing pixel data to transform low-resolution inputs into high-resolution images.
- [Image Super Resolution Models](https://awesome-repositories.com/f/artificial-intelligence-ml/image-super-resolution-models.md) — Increases detail and clarity of low-resolution images using reconstruction algorithms to restore missing information.
- [Image Resolution Reconstruction](https://awesome-repositories.com/f/artificial-intelligence-ml/image-super-resolution-models/high-resolution-synthesis/image-resolution-reconstruction.md) — Restores fine details to pixelated images by estimating high-resolution sources from low-resolution inputs. ([source](https://cdn.jsdelivr.net/gh/spipm/depixelization_poc@main/README.md))
- [Pixel-Wise Difference Optimisations](https://awesome-repositories.com/f/artificial-intelligence-ml/adversarial-loss-functions/l1-pixel-loss/pixel-wise-difference-optimisations.md) — Uses loss-function optimization to minimize the difference between reconstructed pixels and high-resolution ground truth.
- [Feature Map Upsamplers](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-alignment/feature-map-upsamplers.md) — Employs pixel-shuffle upsampling to rearrange channel dimensions into spatial dimensions and reduce artifacts.

### Business & Productivity Software

- [Deep Learning Upscalers](https://awesome-repositories.com/f/business-productivity-software/desktop-application-enhancers/resolution-upscalers/deep-learning-upscalers.md) — Transforms small or pixelated images into larger versions while estimating original high-resolution details.

### Graphics & Multimedia

- [AI Upscaling](https://awesome-repositories.com/f/graphics-multimedia/image-editing-processing/image-enhancement-tools/ai-upscaling.md) — Increases the resolution of small or pixelated images by estimating missing visual details using deep learning.
- [Pixel-Shuffle Utilities](https://awesome-repositories.com/f/graphics-multimedia/image-editing-processing/image-enhancement-tools/ai-upscaling/pixel-shuffle-utilities.md) — Rearranges channel dimensions into spatial dimensions to increase resolution without checkerboard artifacts.
- [Tensor Processing Pipelines](https://awesome-repositories.com/f/graphics-multimedia/image-processing-pipelines/tensor-processing-pipelines.md) — Handles image batches as multi-dimensional arrays to allow parallel processing of pixels across graphics processing units.
- [Parallel Processing Pipelines](https://awesome-repositories.com/f/graphics-multimedia/image-to-tensor-conversions/parallel-processing-pipelines.md) — Incorporates a tensor-based data pipeline for parallel processing of image batches on graphics processing units.