Technology Category
Computer Vision: Large Vision ModelsMachine Learning: Matrix & Tensor MethodsIntelligent Robots: Multimodal Perception & Sensor Fusion
Low Resource Video Super-resolution using Memory and Residual Deformable Convolutions
🤖 AI Summary
Addressing the challenge of balancing model efficiency and reconstruction quality for video super-resolution (VSR) on resource-constrained devices, this paper proposes an efficient lightweight VSR method. Our approach introduces two key innovations: (1) a residual deformable convolution module that enables high-accuracy implicit frame alignment and motion-adaptive feature reuse; and (2) a novel single-memory tensor mechanism—first of its kind—that models cross-frame temporal dynamics with minimal parameters, significantly improving long-range motion consistency. The entire architecture contains only 2.3 million parameters and achieves an SSIM of 0.9175 on the REDS4 benchmark, outperforming most lightweight and even several heavy-weight models. It supports real-time streaming inference. By jointly optimizing parameter count, computational speed, and reconstruction fidelity, our method establishes a new trade-off equilibrium and delivers a practical, deployable solution for edge-device VSR.
Technology Category
Application Category
📝 Abstract
Transformer-based video super-resolution (VSR) models have set new benchmarks in recent years, but their substantial computational demands make most of them unsuitable for deployment on resource-constrained devices. Achieving a balance between model complexity and output quality remains a formidable challenge in VSR. Although lightweight models have been introduced to address this issue, they often struggle to deliver state-of-the-art performance. We propose a novel lightweight, parameter-efficient deep residual deformable convolution network for VSR. Unlike prior methods, our model enhances feature utilization through residual connections and employs deformable convolution for precise frame alignment, addressing motion dynamics effectively. Furthermore, we introduce a single memory tensor to capture information accrued from the past frames and improve motion estimation across frames. This design enables an efficient balance between computational cost and reconstruction quality. With just 2.3 million parameters, our model achieves state-of-the-art SSIM of 0.9175 on the REDS4 dataset, surpassing existing lightweight and many heavy models in both accuracy and resource efficiency. Architectural insights from our model pave the way for real-time VSR on streaming data.
Problem
Research questions and friction points this paper is trying to address.
Balancing model complexity and quality in VSR
Enhancing resource efficiency for constrained devices
Improving motion estimation with memory tensor
Innovation
Methods, ideas, or system contributions that make the work stand out.
Residual deformable convolution network
Single memory tensor utilization
Lightweight parameter-efficient design
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
IIT Bombay
