Our aim is to give a better context to recent discoveries, and to the influence of DL in our domain.
Our algorithm constructs artificial patch-craft images from these bursts by patch matching and stitching, and the obtained crafted images are used as targets for the training.
In this work we introduce a novel stochastic algorithm dubbed SNIPS, which draws samples from the posterior distribution of any linear inverse problem, where the observation is assumed to be contaminated by additive white Gaussian noise.
Our algorithm augments video sequences with patch-craft frames and feeds them to a CNN.
Ranked #4 on Color Image Denoising on CBSD68 sigma25
We showcase our proposed method with a novel denoiser architecture that achieves the reformed denoising goal and produces vivid and diverse outcomes in immoderate noise levels.
Image denoising is a well-known and well studied problem, commonly targeting a minimization of the mean squared error (MSE) between the outcome and the original image.
This work proposes a novel lightweight learnable architecture for image denoising, and presents a combination of supervised and unsupervised training of it, the first aiming for a universal denoiser and the second for adapting it to the incoming image.
Recent work in image processing suggests that operating on (overlapping) patches in an image may lead to state-of-the-art results.