Upscaling images as simply and quickly as possible using the smallest resources
26The purpose of upscaling models is to create a higher resolution, sharper and more detailed image from a low resolution (pixelated, loss of detail) image. This process is known as "up-scaling" or "up-scaling".
These models often use deep neural networks to learn how to improve image quality. The most commonly used approaches are:
Learning by examples: Models are trained with huge amounts of image data, where the relationship between low and high resolution images is known. The model learns what patterns it can use to correctly reconstruct the details.
Convolutional Neural Networks (CNNs): Most of these models use convolutional layers that analyze the image using various filters. These filters can detect patterns such as edges, textures, and colors and use them to increase the detail of the image.
Generative models: Some models, such as BSRGAN or SwinIR, use a generative approach. In this case, the model not only performs a simple image sharpening, but also generates new details to make the image more realistic. Based on previous image information, these models assume what details are missing and complete them.
In the Chainner application, you can use three types of model formats for image upscaling: PyTorch, ONNX, and NCNN. They are all neural network frameworks or formats and are optimized for specific use cases.
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** Links **
chaiNNer: https://github.com/chaiNNer-org/chaiNNer
Upscayl: https://github.com/upscayl/upscayl
Upscayl egyedi modellek: https://github.com/upscayl/custom-mod...
OpenModelDB: https://openmodeldb.info/
4xNomos2_hq_dat2: https://openmodeldb.info/models/4x-No...
4xNomos8kDAT: https://openmodeldb.info/models/4x-No...
4xUltraSharp: https://openmodeldb.info/models/4x-Ul...
4x-SPANkendata: https://openmodeldb.info/models/4x-SP...
Real HAT GAN sharper: https://drive.google.com/drive/folder...
4xRemacri: https://openmodeldb.info/models/4x-Re...
4xNMKD Superscale: https://openmodeldb.info/models/4x-NM...
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