In this paper we suggest how several competing image denoising algorithms, differing in design parameters, or even in design principles, can be combined together to yield a better and more reliable denoising algorithm. The proposed fusion mechanism allows one to combine practically all kinds of noise reduction tools. It also allows one to account for the distribution of measurement noise, and in particular - to cope with heavy-tailed...

The electrocardiography examination, due to its accessibility and simplicity, has an important role in diagnostics of the heart ailments. It enables quick detection of various heart defects, undetectable by other kinds of diagnostic tools, so it is very popular. Nevertheless, the measured signal is exposed to a different disturbances. Among them, the electromagnetic interferences, drift of reference electrode and high frequency...

These paper presents research results of muscle EMG signal denoising. In the same time two muscles were examined - an adductor muscle (biceps brachii) and an abductor muscle (tricpeps brachii). The EMG signal was filtered using the wavelet transform technique, having selected the crucial parameters as: wavelet basis function (Daubechies 4), 10 th decomposition level, threshold selection algorithm (Heurestic) and a sln rescaling...

Dynamic scene video deblurring is a challenging task due to the spatially variant blur inflicted by independently moving objects and camera shakes. Recent deep learning works bypass the ill-posedness of explicitly deriving the blur kernel by learning pixel-to-pixel mappings, which is commonly enhanced by larger region awareness. This is a difficult yet simplified scenario because noise is neglected when it is omnipresent in a wide...

The paper analyzes some properties of denoising autoencoders using the problem of misspellings correction as an exemplary task. We evaluate the capacity of the network in its classical feed-forward form. We also propose a modification to the output layer of the net, which we called multi-softmax. Experiments show that the model trained with this output layer outperforms traditional network both in learning time and accuracy. We...