关键词: 运动模糊, 联合插值去模糊, CNN+Transformer, 轻量化

Abstract: In the realm of dynamic scene image acquisition,consumer-grade imaging devices commonly suffer from a compound degradation problem characterized by coexisting limited frame rates and motion blur.These issues primarily stem from inherent hardware architecture deficiencies and suboptimal exposure parameter optimization.Prevailing methodologies aimed at mitigating these degradations encounter significant technical bottlenecks in real-world blurry scenarios,including insufficient reconstruction accuracy,restricted model generalization capabilities,and low computational efficiency.To address these limitations,this paper introduces FFiT(Faster Frame Interpolation Transformer),a novel joint frame interpolation and motion decoupling optimization framework based on the FasterViT architecture.FFiT is designed to achieve efficient spatio-temporal joint modeling of dynamic blurry sequences.The framework integrates a CNN＋Transformer hybrid encoding architecture,incorporating several key mo-dules:1)An improved multi-scale residual transformer block(MRTB),which leverages spatio-temporal self-attention mechanisms to enhance reconstruction accuracy and explicitly model event correlations between blurry frames;2)A high-quality feature transmitter(HQFT) module,employing a cross-scale feature distillation mechanism to bolster semantic consistency during the blurry-to-sharp domain conversion,thereby addressing the challenge of model generalization;3)A lightweight dynamic upsampling and rendering module(DYRM) that utilizes differentiable dynamic convolution to decouple resolution reconstruction from computational complexity,thus tackling computational inefficiency and enabling flexible resolution recovery.Experimental evaluations on the Adobe240 and RBI datasets demonstrate that FFiT exhibits exceptional performance in addressing the intricate spatio-temporal joint modeling of dynamic blurry sequences.Notably,FFiT reduces model parameters by 70% to accommodate computational resource constraints while achieving a 2.73 dB improvement in PSNR(Peak Signal-to-Noise Ratio) compared to baseline models.This research offers a valuable reference for resolving such compound degradation problems by effectively balancing image quality enhancement with computational efficiency.

Key words: Motion blur, Joint interpolation deblurring, CNN+Transfomer, Lightweight