  The term blind denoising refers to the fact that the basis used for denoising
is learnt from the noisy sample itself during denoising. Dictionary learning
and transform learning based formulations for blind denoising are well known.
But there has been no autoencoder based solution for the said blind denoising
approach. So far autoencoder based denoising formulations have learnt the model
on a separate training data and have used the learnt model to denoise test
samples. Such a methodology fails when the test image (to denoise) is not of
the same kind as the models learnt with. This will be first work, where we
learn the autoencoder from the noisy sample while denoising. Experimental
results show that our proposed method performs better than dictionary learning
(KSVD), transform learning, sparse stacked denoising autoencoder and the gold
standard BM3D algorithm.
