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Found 4 papers, 2 papers with code
Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization
The results show that the proposed multiscale contrastive loss is effective in improving the performance of S2L, which is comparable to that of the supervised learning segmentation method.
I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images
Although the advances of self-supervised blind denoising are significantly superior to conventional approaches without clean supervision in synthetic noise scenarios, it shows poor quality in real-world images due to spatially correlated noise corruption.
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image Denoising
With the advent of advances in self-supervised learning, paired clean-noisy data are no longer required in deep learning-based image denoising.
Noise2Kernel: Adaptive Self-Supervised Blind Denoising using a Dilated Convolutional Kernel Architecture
In this paper, we propose a dilated convolutional network that satisfies an invariant property, allowing efficient kernel-based training without random masking.
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