LIF-VSR: A Lightweight Framework for Video Super-Resolution with Implicit Alignment and Attentional Fusion

Video super-resolution (VSR) has advanced rapidly in enhancing video quality and restoring compressed content, yet leading methods often remain too costly for real-world use. We present LIF-VSR, a lightweight, near-real-time framework built with an efficiency-first philosophy, comprising economical temporal propagation, a new neighboring-frame fusion strategy, and three streamlined core modules. For temporal propagation, a uni-directional recurrent architecture transfers context through a compact inter-frame memory unit, avoiding the heavy compute and memory of multi-frame parallel inputs. For fusion and alignment, we discard 3D convolutions and optical flow, instead using (i) a deformable convolution module for implicit feature-space alignment, and (ii) a sparse attention fusion module that aggregates adjacent-frame information via learned sparse key sampling points, sidestepping dense global computation. For feature enhancement, a cross-attention mechanism selectively calibrates temporal features at far lower cost than global self-attention. Across public benchmarks, LIF-VSR achieves competitive results with only 3.06 M parameters and a very low computational footprint, reaching 27.65 dB on Vid4 and 31.61 dB on SPMCs.