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where ⊛ denotes a convolution operator.

where ∗ k,ℓ denotes a convolution along time and frequency.

where is a two-dimensional Gaussian function with standard deviation, and * denotes a convolution operator.

where ∗ denotes a convolution, β ε is β ε ( t ) = 1 ε β ( t ε ), β being a positive function with ∫ − ∞ ∞ | λ | j | β d λ < ∞, j = 1, 2 and such that its Fourier transform has support in [ − 1, 1 ], where E [ X i ε ( 0 ) X i ε ] = c j j these processes are 1 ε -dependent.

The * denotes a convolution.

The formation of the final simulated image, I f, can be written as (13) where and constitute the addition of Poisson and Gaussian noise, respectively, denotes a convolution operation, stands for the approximation of the PSF with a Gaussian kernel with sigma, is the background image generated by Eq. 10 and is a phantom image.

We use to denote a convolution and is the expectation with respect to. is the identity matrix of size.

where â k, k ′ [ m ] = a [ m ] e j 2 π M km ∗ a [ m ] e j 2 π M k ′ m (5). is the convolution of the prototype filters on subcarriers k and k′ (we use ∗ to denote a convolution).

where γ t, f) is the time-frequency kernel filter, and the double asterisk denotes a double convolution in t and f.

The operator in the expression denotes a truncated linear convolution operation between the two vector sequences and of length and, respectively.

This terminology is unfortunate because the term "deconvolution" denotes the inversion of a convolution process, a particular kind of integral transform encountered in the field of signal processing [ 59].