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The highlights—the brightest parts of an image—can be brighter, more colorful, and more detailed. [2] The larger capacity for brightness can be used to increase the brightness of small areas without increasing the overall image's brightness, resulting in, for example, bright reflections from shiny objects, bright stars in a dark night scene, and bright and colorful light-emissive objects ...
The first iteration of DLSS is a predominantly spatial image upscaler with two stages, both relying on convolutional auto-encoder neural networks. [21] The first step is an image enhancement network which uses the current frame and motion vectors to perform edge enhancement, and spatial anti-aliasing. The second stage is an image upscaling step ...
Images can often be improved – or even transformed – by using graphics software such as Photoshop or the free GIMP and Paint.NET applications. Many other programs also have photo-enhancing tools, including facilities for semi-automatic image enhancement, so that you need only click a button, or choose a thumbnail, to have a positive effect on image quality.
waifu2x is an image scaling and noise reduction program for anime-style art and other types of photos. [1] waifu2x was inspired by Super-Resolution Convolutional Neural Network (SRCNN). [2] [3] It uses Nvidia CUDA for computing, [4] although alternative implementations that allow for OpenCL [5] and Vulkan [6] have been created.
Fotor One-Tap Photo Enhancer is a tool that is designed to automatically improve image quality and fix common imaging problems. [6] [7] Fotor GoArt is a tool to turn photos into classical painting style artwork. [8] Fotor AI Background Remover is a tool that allows users to remove backgrounds from their images with one click. [9]
Let's Enhance [1] is a Ukrainian start-up [2] which develops an online service driven by artificial intelligence which allows improving images and zooming them without losing quality. [3] According to the developers, [4] they used the super-resolution technology of machine learning. The neural network, trained on a large base of real ...
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