<p>This study introduces a novel nonlinear parabolic equation characterized by a variable growth structure, specifically designed for image restoration and enhancement. Our approach builds upon classical models that employ variable exponent operators, extending their capabilities by incorporating a newly developed nonlinear operator featuring a <i>double-phase</i> flux with variable growth. This novel formulation allows for greater adaptability in modeling complex image structures while maintaining key elements like textures and corners. To establish a solid theoretical foundation, we begin by investigating the solvability of the proposed model. Utilizing the framework of variable exponent Lebesgue and Sobolev spaces, we develop an appropriate functional setting that enables rigorous mathematical analysis. The initial focus of our study is to ensure the well-posedness of the model. As a key result, we employ an approximation approach to prove the existence of a positive weak solution called solution obtained as limit of approximations (SOLA). Beyond theoretical considerations, we validate our model through practical applications in image processing. We conduct extensive numerical experiments on grayscale images to assess its performance in denoising and contrast enhancement. Additionally, we conducted evaluations on a collection of magnetic resonance imaging (MRI) scans, further demonstrating its applicability in medical imaging. Our experimental findings demonstrate that the proposed model consistently outperforms current state-of-the-art approaches, achieving enhanced computational efficiency and greater robustness while delivering superior results in both qualitative visual assessment and quantitative evaluation metrics. The findings emphasize the effectiveness of our model as a promising approach for tackling advanced image restoration and enhancement problems.</p>