Exploiting Temporal Attention Features for Effective Denoising in Videos
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Video denoising has significant applications in diverse domains of computer vision, such as video-based object localization, text detection, and several others. An image denoising approach applied to video denoising results in flickering due to ignoring the temporal aspects of video frames. The proposed method makes use of the temporal as well as the spatial characteristics of video frames to form a two-stage denoising pipeline. Each stage uses a channel-wise attention mechanism to forward the encoder signal to the decoder side. The Attention Block used here is based on soft attention to rank the filters for effective learning. A key advantage of our approach is that it does not require prior information related to the amount of noise present in the video. Hence, it is quite suitable for application in real-life scenarios. We train the model on a large set of noisy videos along with their ground-truth. Experimental analysis shows that our approach performs denoising effectively and also surpasses existing methods in terms of efficiency and PSNR/SSIM metrics. In addition to this, we construct a new dataset for training video denoising models and also share the trained model online for further comparative studies.
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