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Found 4 papers, 2 papers with code
Self-supervised Bayesian Deep Learning for Image Recovery with Applications to Compressive Sensing
In recent years, deep learning emerges as one promising technique for solving many ill-posed inverse problems in image recovery, and most deep-learning-based solutions are based on supervised learning.
Ground-Truth Free Meta-Learning for Deep Compressive Sampling
To further improve the learning on the null space of the measurement matrix, a modified model-agnostic meta-learning scheme is proposed, along with a null-space-consistent loss and a bias-adaptive deep unrolling network to improve and accelerate model adaption in test time.
Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising
Deep denoiser, the deep network for denoising, has been the focus of the recent development on image denoising.
Self-supervised Bayesian Deep Learning for Image Denoising
Built on the Bayesian neural network (BNN), this paper proposed a self-supervised deep learning method for denoising a single image, in the absence of training samples.
