Question about physical applicability of noise models are quite appropriate here.

Indeed, no actual signal or noise realization can be perfectly modeled by a particular distribution, for several reasons: discretized data, limited numbers of samples, the non-perfect behavior of the acquisition chain (nonlinearity, saturation, jitter) are common limits. For instance, the idea that an average of $n$ uncorrelated Gaussian noises reduce in amplitude as $1\sqrt{n}$ is greatly limited by quantization or rounded data.

A second aspect is how tractable algorithms are with a certain noise models. Sometimes, one prefers an optimal tractable model, that may work even for other noises, because the approximation is good enough. The question Filtering performance on Poisson noise with quadratic data-fidelity was for instance related to the applicability of a Gaussian noise assumption for Poisson noises.

Question about physical applicability of noise models are quite appropriate here.

Indeed, no actual signal or noise realization can be perfectly modeled by a particular distribution, for several reasons: discretized data, limited numbers of samples, the non-perfect behavior of the acquisition chain (nonlinearity, saturation, jitter) are common limits. For instance, the idea that an average of $n$ uncorrelated Gaussian noises reduce in amplitude as $1\sqrt{n}$ is greatly limited by quantization or rounded data.

A second aspect is how tractable algorithms are with a certain noise models. Sometimes, one prefers an optimal tractable model, that may work even for other noises, because the approximation is good enough. The question Filtering performance on Poisson noise with quadratic data-fidelity was for instance related to the applicability of a Gaussian noise assumption for Poisson noises.

The question about physical applicability of noise models are quite appropriate.

Indeed, no actual signal or noise realization can be perfectly modeled by a particular distribution, for several reasons. Discretized data, a limited number of samples, the non-perfect behavior of the acquisition chain are common limits. For instance, the idea that a sum of $n$ Gaussian noises reduce in amplitude as $1\sqrt{n}$ is greatly limited by quantization or rounded data.

A second aspect is how tractable algorithms are with a certain noise model. Sometimes, one prefers an optimal tractable model, that may work even for other noises, because the approximation is good enough. The question Filtering performance on Poisson noise with quadratic data-fidelity was for instance related to the applicability of a Gaussian assumption for Poisson noises.

Question about physical applicability of noise models are quite appropriate here.

Indeed, no actual signal or noise realization can be perfectly modeled by a particular distribution, for several reasons: discretized data, limited numbers of samples, the non-perfect behavior of the acquisition chain (nonlinearity, saturation, jitter) are common limits. For instance, the idea that an average of $n$ uncorrelated Gaussian noises reduce in amplitude as $1\sqrt{n}$ is greatly limited by quantization or rounded data.

A second aspect is how tractable algorithms are with a certain noise models. Sometimes, one prefers an optimal tractable model, that may work even for other noises, because the approximation is good enough. The question Filtering performance on Poisson noise with quadratic data-fidelity was for instance related to the applicability of a Gaussian noise assumption for Poisson noises.