2), where MRVSR is 0.56dB behind the unconstrained similar network RLSP. {\displaystyle \{{\overline {x}}\}} This recording and stimulation platform allowed us to evoke stable single-neuron responses to chronic electrical stimulation and to carry out longitudinal studies of brain aging in freely. {\displaystyle \{x\}} The high-resolution frame is reconstructed based on both natural preferences and estimated motion. The aim is to improve the resolution of a low-resolution (LR) image/video to obtain a high-resolution (HR) one which is able to preserve the characteristics of natural images/videos. the best of our knowledge, no study about VSR pointed out this instability 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). However, at the end of the sequences these networks and FRVSR have diverged and perform worse than RFS3. [28] Gaussian MRF can smooth some edges, but remove noise.[29]. This shows the contractive recurrence map of MRVSR additionally enables increased temporal consistency. Benjamin Naoto Chiche, et al. Some methods use Fourier transform, which helps to extend the spectrum of captured signal and though increase resolution. The key idea is to use all possible positions as a weighted sum. We demonstrate it on a new long sequence dataset Quasi-Static Video Set, that we have created. We experimentally verified its stability and state-of-the-art performance on long sequences with low motion. Expand 71 Highly Influential PDF { Pixel shuffling rearranges elements in a tensor of shape. A process called demosaicing is used to reconstruct the photos from partial color information. These 4 sequences respectively have the following lengths in number of frames: 379, 379, 379 and 172. People are asked to compare the corresponding frames, and the final mean opinion score (MOS) is calculated as the arithmetic mean overall ratings. In the case of this network, we use the pre-trained weights available on its official github repository. The recurrent information htRn and the output image ^ytRc are updated at each time step t as follows: where xt[0,1]d is an input image provided at time t. The recurrent model is Lipschitz stable if L is contractive in h i.e. Paper. As an implementation of the proposed framework, we design a new network coined Middle Recurrent Video Super-Resolution (MRVSR). Input LR frames, for the residual connection. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Early reconstruction frames receive less temporal information, resulting in fuzzy or artifact results. Recurrent convolutional neural networks perform video super-resolution by storing temporal dependencies. Finally, sliding-window based VSR methods generate independent output HR frames, which reduces temporal consistency of the produced HR frames, resulting in flickering artifacts. L is contractive in h based on the hard Lipschitz constraint: k[[1,K]], ||Wk||1. To solve this issue, we defined a new framework of recurrent VSR model, based on Lipschitz stability theory. 1, in these networks is reduced to the identity mapping (followed by pixel shuffling or transposed convolutions). Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Its architecture is illustrated in Fig. To conclude this section, the following points summarize the limits of existing works regarding long-term recurrent VSR and our contributions: existing recurrent VSR networks have been only evaluated on relatively short generic sequences. When inferring on long sequences, these details keep accumulating long after the short-term networks training regime, which produces visible artifacts that diverge over time. The third part has a feed-forward architecture with n convolutional layers interlaced with ReLU activations and followed by a pixel shuffling layer. While SISR aims to generate a high-resolution (HR) image from its low-resolution (LR) version, in VSR the goal is to reconstruct a sequence of HR images from the sequence of their LR counterparts. The tested scale factor is 4. x [8] Projections onto convex sets (POCS), that defines a specific cost function, also can be used for iterative methods. Video super-resolution (VSR) is an inverse problem that extends single-image super-resolution (SISR). This performance is compatible with the results on Vid4 (Tab. This network will serve as baseline against recurrent models. 1 and3) MRVSR can not match RLSP and RSDN, but performs better than the baseline RFS3 and FRVSR. Also proposed a few new metrics: ERQAv1.0, QRCRv1.0, and CRRMv1.0. Moreover, an inference of a recurrent model presents less redundant computations than the one of a sliding-window based model because each frame is processed only once. Video super-resolution (VSR) is an inverse problem that extends single-image super-resolution (SISR). Second, we did the same experiment with (,)=(1.0,1.0) to enforce HL and this resulted in a stable network but with poor VSR performance (detailed in Sec. The resolution of ground-truth frames is 19201080. maximal singular value of the reshaped kernel tensor of the convolutional layer. Qualitative evaluation that checks the presence of artifacts is of equal importance. In contrast to FRVSR, RLSP is based on implicit motion compensation. 3). if L is L-Lipschitz in h with L<1 (the superscript in L highlights this Lipschitz continuity). In order to measure the benefit from constrained recurrence map, we also implement MRVSR without its recurrence and feature-shifting, which coincides with RLSP without its recurrence mecanism. This is the case encountered when imposing HL on all convolutional layers of networks such as RLSP, FRVSR and RSDN. HL: RLSP-HL also obtains an overall poor performance (1.13dB in average PSNR and 0.0278 in average SSIM compared to RFS3 based on all reconstructed frames, according to Tab. When working with video, temporal information could be used to improve upscaling quality. In the first sequence of the Quasi-Static Video Set, the bird moves regularly, which is why artifacts do not have time to appear on the bird itself, as can be seen on Fig. It is performed by consideration of patches similarities. diverge through recurrent processing, generating high frequency artifacts. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. k[[1,K]], we set ||Wk||=>1 and minimize srank(Wk) based on training data, where srank is the Stable rank. We limited the lengths of the sequences to 379 to ensure dataset homogeneity, but the video containing the first sequence contains a much larger number of frames. 4(f), 6 and3 show that MRVSR does not diverge and does not generate any artifact. We thus adapted the codes of the corresponding degradations that are available on this repository to generate the LR sequence and the value of =1.6 was used. There are many approaches for this task, but this problem still remains to be popular and challenging. Compared to frame-recurrence, RLSP can be interpreted as maximizing the depth and width of the recurrent connection. The resolution of ground-truth frames is 1280720. Based on reported performances, at the beginning of the sequences RLSP and RSDN perform better than the baseline RFS3. share 9 research 2 years ago To the best of our knowledge, this work is the first study about VSR that raises this instability issue. GT TOFlow DUF-52LRBPN EDVR-L PFNL FRVSR 10-128RLSP 7-256 Ours 9-128 Bicubic Ours 5-128Ours 7-128 Ours 5-128 Ours 7-128 Ours 9-128 RLSP 7-256 [12] One can also use steepest descent,[13] least squares (LS),[14] recursive least squares (RLS). The differences in performance on the last 50 reconstructed frames between RFS3 and respectively RLSP, FRVSR and RSDN are 1.50, 4.39 and 4.09 in PSNR and 0.0029, 0.0790 and 0.0362 in SSIM. We empirically show its competitive performance on long sequences with low motion. 7, where the temporal receptive field of MRVSR spans around 28 frames, which is much larger than the usual length (i.e. Some examples can be observed in Fig. Experiments show that our strategy outperforms an alternative nave approach of encoding all FET frames as is and performing temporal super-resolution at decoder by up to 1.1dB at the same bitrate. This benchmark tests models' ability to work with compressed videos. Evolution of PSNR on Y channel per frame averaged over the first three sequences of the Quasi-Static Video Set. 5.2). The features zt, the hidden state ht and the output image ^yt are updated at each time step t as follows: where Xt={xt}tTtt+T[0,1]d(2T+1) is an input batch of LR images provided to the network at t and 2T+1 denotes the size of the batch. However, these iterative algorithms are relatively slow and not suitable for real-world applications. A more recent recurrent VSR architecture called recurrent latent space propagation (RLSP) was introduced in[fuoli2019efficient]. In this approach, the previous output frame and the previously estimated locality based hidden state are used as an extra input at the next time step. We propose a new recurrent VSR network, coined Middle Recurrent Video Super-Resolution (MRVSR), based on this framework. Super Resolution techniques help to restore the original video. We numerically evaluate the networks based on frame PSNR and SSIM. 17 models were tested. For comparison, we implement the following state-of-the-art recurrent VSR networks in Pytorch. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Finally, we empirically analyze instabilities of existing recurrent VSR models on long sequences with low motion and show the stability and superior performance of the proposed network. pressing it against the window) and maximally pinch-zooming the viewfinder. Motion estimation gives information about the motion of pixels between frames. The first two of them are respectively Full HD and HD Ready and the two others are 4K. Finally, we introduce a Top methods are performed in the table: The MSU Video Super-Resolution Benchmark was organized by MSU and proposed three types of motion, two ways to lower resolution, and eight types of content in the dataset. It also helps to solve task of object detection, face and character recognition (as preprocessing step). Stable Long-Term Recurrent Video Super-Resolution. In the presence of strong motion, even with short-term training, the network learns to forget the past information, which is inconsistent with the new one.
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