spipm/Depixelization_poc
Depixelization Poc
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.
Features
- Super Resolution - Provides a super-resolution framework that transforms low-resolution inputs into high-resolution versions by estimating missing pixels.
- Convolutional Neural Networks - Implements a convolutional neural network pipeline to analyze pixel data and recover high-frequency image details.
- Image Restorers - Recovers lost detail from degraded or compressed images to make them appear sharper and more natural.
- Deep Learning Image Processors - Utilizes convolutional neural networks and tensor-based pipelines to analyze and modify pixel data for enhancement.
- Image Restoration Models - Implements a reconstruction system that estimates missing pixel data to transform low-resolution inputs into high-resolution images.
- Image Super Resolution Models - Increases detail and clarity of low-resolution images using reconstruction algorithms to restore missing information.
- Image Resolution Reconstruction - Restores fine details to pixelated images by estimating high-resolution sources from low-resolution inputs.
- Deep Learning Upscalers - Transforms small or pixelated images into larger versions while estimating original high-resolution details.
- AI Upscaling - Increases the resolution of small or pixelated images by estimating missing visual details using deep learning.
- Pixel-Wise Difference Optimisations - Uses loss-function optimization to minimize the difference between reconstructed pixels and high-resolution ground truth.
- Feature Map Upsamplers - Employs pixel-shuffle upsampling to rearrange channel dimensions into spatial dimensions and reduce artifacts.
- Image Reconstruction - Uses transformer-based reconstruction techniques to restore high-resolution details to pixelated images.
- Pixel-Shuffle Utilities - Rearranges channel dimensions into spatial dimensions to increase resolution without checkerboard artifacts.
- Tensor Processing Pipelines - Handles image batches as multi-dimensional arrays to allow parallel processing of pixels across graphics processing units.
- Parallel Processing Pipelines - Incorporates a tensor-based data pipeline for parallel processing of image batches on graphics processing units.
