2 citations · 3 across the 4 of their papers we have counts for
4 papers
Self-Supervised Single-Image Deconvolution with Siamese Neural Networks
Mikhail Papkov, Kaupo Palo, Leopold Parts
Inverse problems in image reconstruction are fundamentally complicated by unknown noise properties. Classical iterative deconvolution approaches amplify noise and require careful p…
Metadata Improves Segmentation Through Multitasking Elicitation
Iaroslav Plutenko, Mikhail Papkov, Kaupo Palo +2
Metainformation is a common companion to biomedical images. However, this potentially powerful additional source of signal from image acquisition has had limited use in deep learni…
SwinIA: Self-Supervised Blind-Spot Image Denoising without Convolutions
Mikhail Papkov, Pavel Chizhov, Leopold Parts
Self-supervised image denoising implies restoring the signal from a noisy image without access to the ground truth. State-of-the-art solutions for this task rely on predicting mask…
Noise2Stack: Improving Image Restoration by Learning from Volumetric Data
Mikhail Papkov, Kenny Roberts, Lee Ann Madissoon +4
Biomedical images are noisy. The imaging equipment itself has physical limitations, and the consequent experimental trade-offs between signal-to-noise ratio, acquisition speed, and…