Mixed noise removal via generalized gaussian scale mixture modeling and plug-and-play deep prior

Restoring images corrupted by a combination of additive white Gaussian noise (AWGN) and salt-and-pepper impulse noise (SPIN) poses a significant challenge, primarily stemming from the complexities involved in accurately modeling the distributions of the mixed noise. Traditional methods for mixed noise removal, such as filters and optimization models, often exhibit high computational complexity and limited performance. Inspired by the strong performance of deep learning-based approaches, we propose a novel mixed noise removal method that leverages an implicit deep image prior, called GGSM-PnP. Specifically, drawing inspiration from empirical distributions, we model the mixed noise using the generalized Gaussian distribution and establish the model within a maximum a posteriori framework. Subsequently, we employ the alternating direction multiplier method to derive the algorithm for solving the proposed model. Within the deep prior involved sub-problem, we integrate an offline-trained Gaussian denoiser into the plug-and-play framework. Experimental results on synthetic noisy images demonstrate the superior performance of the proposed method compared to existing techniques for removing mixed AWGN+SPIN noise.