A geometric function theoretic approach to MRI distortion correction using Miller Ross Poisson series

This study focuses on the newly introduced subclass of Sakaguchi-type function associated with the Miller–Ross-type Poisson distribution series. Using the proposed operator, we derive several coefficient bound, inverse function estimates, initial logarithmic coefficient results and Feketo-Szego type inequalities. Furthermore, growth, distortion, convex combination properties, subordination outcomes, and partial sum results are derived for the defined class. Application part demonstrates how the obtained coefficient bounds can be employed to design an analytic correction operator for improving the quality of distorted Magnetic Resonance Imaging (MRI) scans. Numerical experiments on real datasets validate the effectiveness of the proposed model, showing significant improvement in terms of MSE, PSNR, and SSIM. These results highlight the interplay between geometric function theory and practical imaging problems, thereby providing a new perspective on the use of analytic function subclasses in applied sciences.