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. ...

ESRGAN stands for Enhanced Super-Resolution Generative Adversarial Network and is a foundational project in the field of deep learning-based image super-resolution. It builds on earlier GAN-based approaches by improving network architecture (e.g., using Residual-in-Residual Dense Blocks), adversarial loss functions, and perceptual loss components to generate higher-fidelity high-resolution images from low-resolution inputs with more realistic textures and details.

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.

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.

This is showcase program, with source code and sample data included for fast peak detection algorithm. It is great tool for entry level, automated analisys, to discard empty and junk curves and save only ones matching required criteria. Assuming that only 1% of data is worth reviewing, and that manual selection takes approx. 3 seconds per curve, you can save up to 8 hours per every 10000 curves (like 2 days every week!). Force amplitude analisys for single curve takes from 1 to 4...