Video rain removal is a crucial yet highly important step task for various computer vision applications, including video surveillance, autonomous vehicles, and traffic monitoring. The existing methods for video de-raining possess some limitations of prior-dependency (such as optical flow) and multi-frame dependency, which reduces their practicability due to higher computational complexity. We address these issues and propose a novel transformer based efficient video-deraining framework named TransDerain. The key contributions of TransDerain include: Multi-receptive Feature Assisted Transformer Block and the Self-Calibrated Deformable Temporal Alignment Block. Initially, the proposed Multi-receptive Feature Assisted Transformer Block integrates multi-scale convolutional operations with transformer mechanisms to capture both local and global contextual information. Further, the proposed Self-Calibrated Deformable Temporal Alignment Block addresses temporal inconsistencies in video restoration by combining self-calibrated convolutions with deformable convolutions, facilitating precise temporal alignment across video frames. This approach reduces computational complexity and mitigates common artifacts such as ghosting, ensuring stable and coherent video restoration. Experimentation is performed on RainSynComplex and Rain Motion de-raining datasets to demonstrate the effectiveness of the proposed TransDerain method over with existing state-of-the-art approaches.