High-quality video acquisition is essential for emerging applications such as remote sensing, autonomous driving, and augmented reality. However, the sequential readout inherent to CMOS sensors employing rolling shutters introduces severe geometric distortions, particularly during rapid scene motion. Traditional distortion-correction methods either rely on computationally intensive post-processing or necessitate costly global shutter hardware, resulting in significant trade-offs between accuracy, complexity, and cost. To address these limitations, we propose an end-to-end optimization framework integrating a custom optical layer represented by a compact, learnable M × N mask and a neural reconstruction network. By jointly optimizing optical acquisition and digital correction, our method significantly reduces distortion artifacts, achieving up to 4.3 dB PSNR improvement over a conventional rolling shutter fixed acquisition sequence in the test set. Our approach provides a practical and effective solution for high-speed, distortion-free scene capture