Search Results for "super"

Real-ESRGAN is a highly popular open-source project that provides practical algorithms for general image and video restoration using deep learning-based super-resolution techniques. It extends the original Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) approach by training on synthetic degradations to make results more robust on real-world images, effectively enhancing resolution, reducing noise/artifacts, and reconstructing fine detail in low-quality imagery. The repository includes inference and training scripts, a model zoo with different pretrained models (including general and anime-oriented variants), and support for batch and arbitrary scaling, making it adaptable for diverse enhancement tasks. ...

This program benchmarks the following sorting algorithms implemented in Java: Quick Sort, Heap Sort, Merge Sort, Selection Sort, and Bubble Sort. A command line GUI was created to facilitate all your benchmark needs.

The Latin Square Toolbox contains software tools for efficiently generating Latin squares and counting their transversals with various user-configurable options. This open source release aims to help educate folks on Latin squares and their important applications to cryptography. This version contains three tools: 0. Latin Square Generator (LSG) 1. Latin Square Transversal Counter (LSTC) 2. Latin Square Property Checker (LSPC)

A Python module to learn all the major algorithms on the go! Purely for educational purposes. If you are using Python 2.7 use pip instead. Depending on your permissions, you might need to use pip install, user pygorithm to install. To see all the available functions in a module, you can just type help() with the module name as an argument.

...Now, it is possible to use as many cores in a network as you want. With a focus on the MILP methods we implement a load balancing and speed up the solving process in a multiplicative way. Sometimes we have super-linear speedup with a small set of hardware. With a splitting of problems, parallel computing and distributing the actual best solution to all running processes we solve CBP much faster than a sequential processing can do.