A first-order Rician denoising and deblurring model
Wenli Yang, Zhongyi Huang, Wei Dong Zhu · Inverse Problems and Imaging · 2023
In this paper, we propose a first-order variational model for the Rician denoising and deblurring. The model employs a recently developed regularizer that has proven to be effective in image restoration [32]. Due to this regularizer, our model is able to suppress the staircase effect, and more importantly, it helps preserve image contrast, which considerably reduces the blurring effect in restored images. We present the maximum-minimum principle of the model and give a more accurate convex approximation than the conventional one for the fidelity term. Augmented Lagrangian method is utilized to minimize the associated functional. Synthetic and real gray and color images are tested to demonstrate the features of our model.