Kang Yang1, Jielin Jiang1,2,*, Zhaoqing Pan1,2
Journal of New Media, Vol.2, No.1, pp. 1-10, 2020, DOI:10.32604/jnm.2020.09356
Abstract Due to the huge difference of noise distribution, the result of a mixture
of multiple noises becomes very complicated. Under normal circumstances, the
most common type of mixed noise is to add impulse noise (IN) and then white
Gaussian noise (AWGN). From the reduction of cascaded IN and AWGN to the
latest sparse representation, a great deal of methods has been proposed to reduce
this form of mixed noise. However, when the mixed noise is very strong, most
methods often produce a lot of artifacts. In order to solve the above problems, we
propose a method based on residual learning… More >
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