The aim of this project is to develop a learned, convex regularizer for image reconstruction driven by a parametric, steerable wavelet framework. By explicitly controlling filter scale and orientation with a compact parameter set, the proposed model efficiently captures directional image features while strictly guaranteeing convexity for stable optimization. The project involves training the regularizer via denoising and validating it across diverse inverse problems using accelerated gradient methods.