Adaptive-autoencoder-dictionary-learning I took Responsibility to solve real-time video denoising and compression problems, especially in the insufficient light scenario. When light is insuffient, the ...

We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE), that solves convolutional dictionary learning problems, thus establishing a link between ...

To improve the efficiency of the super resolution algorithm based on dictionary learning, a super-resolution algorithm combining sparse autoencoder dictionary learning and anchored neighborhood ...

This learning form represents input data (for instance, a noisy image) as individual elements, and these elements (image patches) are termed atoms that collectively constitute the ‘dictionary’.