Search results
Results from the WOW.Com Content Network
Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction , super-resolution , and inpainting .
Originally, super-resolution methods worked well only on grayscale images, [18] but researchers have found methods to adapt them to color camera images. [17] Recently, the use of super-resolution for 3D data has also been shown.
Video super-resolution (VSR) is the process of generating high-resolution video frames from the given low-resolution video frames. Unlike single-image super-resolution (SISR) , the main goal is not only to restore more fine details while saving coarse ones, but also to preserve motion consistency.
The second stage is an image upscaling step which uses the single raw, low-resolution frame to upscale the image to the desired output resolution. Using just a single frame for upscaling means the neural network itself must generate a large amount of new information to produce the high resolution output, this can result in slight hallucinations ...
Scalable Vector Graphics are well suited to simple geometric images, while photographs do not fare well with vectorization due to their complexity. Note that the special characteristics of vectors allow for greater resolution example images. The other algorithms are standardized to a resolution of 160x160 and 218x80 pixels respectively.
Super-resolution microscopy is a series of techniques in optical microscopy that allow such images to have resolutions higher than those imposed by the diffraction limit, [1] [2] which is due to the diffraction of light. [3]
Image credits: domestika.org #22 With 'YouTube Success: Script, Shoot & Edit with MKBHD' , become a Youtube star! Learn to create engaging videos and grow your channel from scratch to 18M+ subs ...
Super-resolution optical fluctuation imaging (SOFI) is a post-processing method for the calculation of super-resolved images from recorded image time series that is based on the temporal correlations of independently fluctuating fluorescent emitters.