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In this study, we propose a self-supervised video denoising method called"restore-from-restored." This method fine-tunes a pre-trained network by usinga pseudo clean video during the test phase. The pseudo clean video is obtainedby applying a noisy video to the baseline network. By adopting a fullyconvolutional neural network (FCN) as the baseline, we can improve videodenoising performance without accurate optical flow estimation and registrationsteps, in contrast to many conventional video restoration methods, due to thetranslation equivariant property of the FCN. Specifically, the proposed methodcan take advantage of plentiful similar patches existing across multipleconsecutive frames (i.e., patch-recurrence); these patches can boost theperformance of the baseline network by a large margin. We analyze therestoration performance of the fine-tuned video denoising networks with theproposed self-supervision-based learning algorithm, and demonstrate that theFCN can utilize recurring patches without requiring accurate registration amongadjacent frames. In our experiments, we apply the proposed method tostate-of-the-art denoisers and show that our fine-tuned networks achieve aconsiderable improvement in denoising performance.

Restore from Restored: Video Restoration with Pseudo Clean Video