基于弹性权重巩固的视频单曝光压缩成像算法研究OA

[Objective]This work aims to design a unified model with high robust hyperparameters,in-cluding compression ratio,modulation mask and measurement resolution,for Snapshot Com-pressive Imaging(SCI).[Methods]We train the proposed model based on Elastic Weight Con-solidation(EWC).The model is uniquely designed by combining Transformer and Convolution-al neural network architectures.Additionally,during the initialization phase,we incorporate Generalized Alternating Projection to enhance the model's robustness to different masks.[Results]Extensive ex-perimental results demonstrate that our proposed unified model can well adapt to different compression ratios,modula-tion masks,and measurement resolutions while achieving state-of-the-art results.Our model outperforms previous SO-TA algorithms in terms of PSNR and SSIM,with an average PSNR improvement of over 5 dB.[Limitations]Al-though our model outperforms previous SOTA algorithms in terms of adaptability and average PSNR,the model with EWC may not perform better than a model specifically trained for a particular single task.[Conclusions]By introduc-ing Generalized Alternating Projection and EWC,as well as the special design of the network structure,our proposed highly adaptive model provides new ideas and methods for solving compressive sensing reconstruction tasks in other complex scenarios.