| CPC G06T 5/70 (2024.01) [G06T 3/4084 (2013.01); G06T 5/50 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20182 (2013.01)] | 16 Claims |
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1. A computer-implemented method for forming a dataset configured for learning a Convolutional Neural Network (CNN) architecture, the CNN architecture comprising an image feature extractor, the method comprising:
obtaining pairs of images, each pair comprising a reference image and a respective denoised image, the reference image being a Monte-Carlo rendered ray-traced image, the respective denoised image being a result of inputting a respective noisy image of the reference image to a denoiser; and
for each pair of images:
providing the pair of images to a pre-trained CNN architecture similar to the one for which the formed dataset will be configured,
computing a difference between a first normalized feature of the denoised image and a second normalized feature of the reference image, the first and second normalized features being an output of a same layer of the pre-trained CNN architecture,
computing an error map representing the computed difference, computing the error map including:
down-sampling a resolution of the computed difference, and
computing the error map with the down-sampled resolution of the difference by creating an image having pixel values, each pixel of the error map having a color following a color scale that penalizes the computed difference, thereby obtaining a coarse error map, and
adding the respective denoised image and the error map to the dataset.
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