Reconstruction algorithms based on proximal optimization are critical to many imaging systems across science and medicine. Identifying strategies to reduce their computational demands is therefore of utmost importance. In our previous work [1], we identified that some common image regularizers admit proximal operators that combine well with nonnegativity constraints, enabling reduced splitting and thereby cheaper and faster computations. The goal of this project is to empirically explore other common regularizers and their proximal operators in order to discover more cases in which performance can be improved. Depending on the evolution of the project and the preferences of the student, there is an option to delve on the underlying theory [2]. The student should be a proficient programmer, and the preferred development languages are Python or Matlab. References: [1]: Pol del Aguila Pla and Joakim Jaldén, Cell detection by functional inverse diffusion and non-negative group sparsity—Part II: Proximal optimization and Performance evaluation, IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5422-5437, 2018 [2]: S. Adly, L. Bourdin, and F. Caubet, On a decomposition formula for the proximal operator of the sum of two convex functions,” Journal of Convex Analysis, vol. 26, no. 3, pp. 699-718, 2019.