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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 .
A very common type of prior knowledge in pattern recognition is the invariance of the class (or the output of the classifier) to a transformation of the input pattern. This type of knowledge is referred to as transformation-invariance. The mostly used transformations used in image recognition are: translation; rotation; skewing; scaling.
Deep image compositing is a way of compositing and rendering digital images that emerged in the mid-2010s. In addition to the usual color and opacity channels a notion of spatial depth is created. This allows multiple samples in the depth of the image to make up the final resulting color.
Traditionally, SNR is defined to be the ratio of the average signal value to the standard deviation of the signal : [2] [3] = when the signal is an optical intensity, or as the square of this value if the signal and noise are viewed as amplitudes (field quantities).
Deep learning neural network-based inpainting can be used for decensoring images. [14] Deep image prior-based techniques can be used for digital image inpainting, where a trained deep learning model is either unavailable or infeasible. Three main groups of 2D image-inpainting algorithms can be found in the literature.
The deep dive. A view of Bourbon Street following the Jan. 1 terrorist attack. ... more than 100 bomb-sniffing dogs have been scouring the Superdome and each of its more than 70,000 seats prior to ...
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