Posterior-Mean Rectified Flow (PMRF) is an innovative algorithm designed for photo-realistic image restoration, focusing on minimizing the Mean Squared Error (MSE) while ensuring high perceptual quality. The algorithm is developed by researchers Guy Ohayon, Tomer Michaeli, and Michael Elad from the Technion—Israel Institute of Technology. PMRF operates by first predicting the posterior mean of a degraded image, which could be affected by noise or blurriness. This prediction represents the reconstruction that minimizes MSE. Following this, the algorithm employs a rectified flow model to transport the predicted result to a high-quality image. The training process for PMRF is structured in two consecutive stages, each requiring the minimization of a straightforward MSE loss. In the realm of photo-realistic image restoration, algorithms are typically assessed using both distortion measures, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), as well as perceptual quality measures like Fréchet Inception Distance (FID) and Naturalness Image Quality Evaluator (NIQE). The challenge lies in achieving minimal distortion without sacrificing perceptual quality. Many existing methods attempt to sample from the posterior distribution or optimize a combination of distortion and perceptual quality losses. However, PMRF distinguishes itself by focusing on the optimal estimator that minimizes MSE under the constraint of achieving perfect perceptual quality, where the distribution of the reconstructed images matches that of the ground-truth images. The theoretical foundation of PMRF is based on a recent result that indicates an optimal estimator can be constructed by effectively transporting the posterior mean prediction to align with the distribution of the ground-truth images. This insight inspired the development of PMRF, which not only approximates this optimal estimator but also demonstrates superior performance compared to previous methods across various image restoration tasks, including colorization, inpainting, denoising, and super-resolution. The research findings are documented in a paper available on arXiv, highlighting the effectiveness and theoretical utility of PMRF in the field of image restoration. The authors have made their work accessible through various platforms, including a dedicated website and code repositories, facilitating further exploration and application of their algorithm.