We empirically show its competitive performance on long sequences with low motion. However, when inferring on long video sequences presenting low motion (i.e. 6 and3), but the reconstructed image is blurred (Fig. 4. Finally, this result frame is transformed to the spatial domain. We propose a new recurrent VSR network, coined Middle Recurrent Video Super-Resolution (MRVSR), based on this framework. This is because RLSP-HL is globally constrained to be 1-Lipschitz. Its reconstruction performance is generally stable on a long sequence (Figs. ", MOVIE (Motion-based Video Integrity Evaluation index), VMAF (Video Multimethod Assessment Fusion), MSU Video Super-Resolution Benchmark 2021, MSU Super-Resolution for Video Compression Benchmark 2022, "Deformable Non-Local Network for Video Super-Resolution", "A multiresolution mixture generative adversarial network for video super-resolution", "Residual Invertible Spatio-Temporal Network for Video Super-Resolution", "Bidirectional Temporal-Recurrent Propagation Networks for Video Super-Resolution", "Video superresolution with nonlocal alignment network", "See Better and Further with Super Res Zoom on the Pixel 3", https://en.wikipedia.org/w/index.php?title=Video_super-resolution&oldid=1120327258, Creative Commons Attribution-ShareAlike License 3.0. Request PDF | On Jun 1, 2022, Benjamin Naoto Chiche and others published Stable Long-Term Recurrent Video Super-Resolution | Find, read and cite all the research you need on ResearchGate Video Super-Resolution (VSR) is the process of generating high-resolution video frames from the given low-resolution ones. {\displaystyle \{x\}} When considering long-term performance on sequences with low motion, MRVSR gives the best results. When working with video, temporal information could be used to improve upscaling quality. This optimization only affects pixels in X that have an effect on the value of p. Therefore, the optimized sequence X can be interpreted as a visualization of the STRF for the pixel p. is typically set to 40, pixels in X, are randomly initialized following an uniform random variable between 0 and 1 and images in. Some methods use Fourier transform, which helps to extend the spectrum of captured signal and though increase resolution. [23], Probabilistic methods use statistical theory to solve the task. 2), where MRVSR is 0.56dB behind the unconstrained similar network RLSP. The second part L is composed of n+1 convolutional layers under HL and interspaced ReLU activations. data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAKAAAAB4CAYAAAB1ovlvAAADOUlEQVR4Xu3XQUpjYRCF0V9RcOIW3I8bEHSgBtyJ28kmsh5x4iQEB6/BWQ . While deep learning approaches of video super-resolution outperform traditional ones, it's crucial to form a high-quality dataset for evaluation. Super-resolution is an inverse operation, so its problem is to estimate frame sequence 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. We propose a new recurrent VSR network, coined Middle Recurrent Video Super-Resolution (MRVSR), based on this framework. were pioneers of recurrent VSR. We see that until a relatively small number of processed frames, existing recurrent networks (RLSP, RSDN and FRVSR) perform optimally and remain better than the baseline model. There are many approaches for this task, but this problem still remains to be popular and challenging. Therefore, VSR requires to accumulate information over a number of LR frames as large as possible. {\displaystyle \{{\overline {x}}\}} super-resolution (VSR), due to their increased computational efficiency, Video super-resolution finds its practical use in some modern smartphones and cameras, where it is used to reconstruct digital photographs. Recurrent models have gained popularity in deep learning (DL) based video super-resolution (VSR), due to their increased computational efficiency, temporal receptive field and temporal consistency compared to sliding-window based models. These high frequency details grow in strength with time, but they are not fed back into the network more than 10 times, so the optimization process is not trained to manage their increase after this period. [19] To strength searching for similar patches, one can use rotation invariance similarity measure[20] or adaptive patch size. This unexpected behavior can be critical for some real-world applications, like video surveillance in which both the camera and the scene stay static for a long time. the best of our knowledge, no study about VSR pointed out this instability This seeks to increase the depth and width of the recurrent connection by giving the hidden state and the previous output to the input of super-resolving networks. From each of the training, validation and test sequences in HR space, the corresponding LR sequence is generated by applying gaussian blur with and sampling every s=4 pixel in both spatial dimensions. In this work, we expose instabilities of existing recurrent VSR networks on 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Recurrent models have gained popularity in deep learning (DL) based video super-resolution (VSR), due to their increased computational efficiency, temporal receptive field and temporal consistency, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). 1 summarizes the performances of the networks on the Quasi-Static Video Set. The loss function is pixel-wise mean-squared-error between pixels in the brightness channel Y of YCbCr color space of GT frames and the networks output. In contrast to FRVSR, RLSP is based on implicit motion compensation. We prepare the training dataset in a similar way as in[fuoli2019efficient]. Stable Long-Term Recurrent Video Super-Resolution Recurrent models have gained popularity in deep learning (DL) based vide. 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 HD and 4K sequences are downsampled respectively by a factor of 2 and 4. The 4th (Parallel + Cascade) is our final hybrid local fusion method. The idea behind VSR, which makes it fundamentally different from SISR, is that the fusion of several LR images produces an HR image. Most of the deconvolution task is done by and . L incorporates past information. placed on a tripod). PSNR and VMAF metrics were used for performance evaluation. share 9 research 2 years ago We set =1.5, except when testing RSDN. We propose a new recurrent VSR network, coined Middle Recurrent Video Super-Resolution (MRVSR), based on this framework. To the best of our knowledge, this work is the first study about VSR that raises this instability issue. 2 T.Isobeetal. [9], Iterative adaptive filtering algorithms use Kalman filter to estimate transformation from low-resolution frame to high-resolution one. x These considerations make recurrent methods more interesting from a realistic application-oriented point of view. based on Lipschitz stability theory. In approaches with alignment, neighboring frames are firstly aligned with target one. We demonstrate it on a new long sequence dataset Quasi-Static Video Set, that we have created. 5.2). The table conforms with the curves shown on Fig. A tag already exists with the provided branch name. Figs. As an implementation of the proposed framework, we design a new network coined Middle Recurrent Video Super-Resolution (MRVSR). To enforce these constraints in the context of convolutional neural networks, are among hyperparameters of the algorithm. Example artifacts are shown on Figs. [28] Gaussian MRF can smooth some edges, but remove noise.[29]. Due to the cheap convolution operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame methods. Stable Long-Term Recurrent Video Super-Resolution. long sequences with low motion. The networks are trained with Adam optimizer. The key idea is to use all possible positions as a weighted sum. Blur kernel, downscaling operation and additive noise should be estimated for given input to achieve better results. 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. ICIP (International Conference of Image Processing), Propagation refers to the way in which features are propagated temporally, Alignment concerns on the spatial transformation applied to misaligned images/features, Aggregation defines the steps to combine aligned features, Upsampling describes the method to transform the aggregated features to the final output image, LPIPS (Learned Perceptual Image Patch Similarity) compares the perceptual similarity of frames based on high-order image structure, tOF measures pixel-wise motion similarity with reference frame based on, tLP calculates how LPIPS changes from frame to frame in comparison with the reference sequence, FSIM (Feature Similarity Index for Image Quality) uses, This page was last edited on 6 November 2022, at 12:07. Although. According to Tab. Cb and Cr channels are upsampled independently with bicubic interpolation. 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. In this study, we show that recurrent VSR networks generate high frequency artifacts when inferring on long video sequences presenting low motion. The tested scale factor is 4. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Finally, we introduce a new framework of recurrent VSR networks that is both stable and competitive, based on Lipschitz stability theory. task. 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. Some most essential components for VSR are guided by four basic functionalities: Propagation, Alignment, Aggregation, and Upsampling.[1]. The metrics are measured excluding the first 3 and last 3 GT frames. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. if L is L-Lipschitz in h with L<1 (the superscript in L highlights this Lipschitz continuity). There are broadly two classes of deep VSR methods. In these networks, to super-resolve a frame at time step t, the hidden states and/or output computed in previous time step t1 are taken as input, in addition to a batch of 1 to 3 LR frames. 3 shows the evolution of the PSNR per frame for some of the networks, averaged over the first three sequences of Quasi-Static Video Set. To solve this issue, we defined a new framework of recurrent VSR model, based on Lipschitz stability theory. 1 illustrates this phenomenon. In this setting, it is beneficial to the network to produce rapidly a huge amount of details in the output sequence. 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. Similarities. They are then deployed to super-resolve a sequence of any length. They are less efficient compared to the recurrent-based methods. This mecanism, called feature-shifting, is helpful to promote temporal consistency between two successively output frames. To evaluate models' performance PSNR and SSIM were used. [27] Huber MRFs are used to preserve sharp edges. Abstract: Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Dataset REDS was collected for this challenge. We will make all of these sequences available on https://github.com/bjmch/StableVSR. Some approaches also consider temporal correlation among high-resolution sequence. We compare these networks against the proposed MRVSR. Among MRVSR with different sets (n,n,n), the network with (n,n,n)=(3,1,3) was the best performing model on our validation set. Compared to frame-recurrence, RLSP can be interpreted as maximizing the depth and width of the recurrent connection. The common way to estimate the performance of video super-resolution algorithms is to use a few metrics: Currently, there aren't so many objective metrics to verify video super-resolution method's ability to restore real details. from frame sequence This can be seen as an extension of SISR that takes 3 consecutive LR frames as an input at each timestep. It summarizes the performances of the methods at the beginning of the sequences, through the entire sequences, and at the end of the sequences. SL: RLSP-SL faces the same issues as existing recurrent networks. There are situations where hand motion is simply not present because the device is stabilized (e.g. The delimiting keyframes are excluded from the sequence. The NTIRE 2019 Challenge was organized by CVPR and proposed two tracks for Video Super-Resolution: clean (only bicubic degradation) and blur (blur added firstly). Moreover, considering that RFS7 takes an input batch of 7 frames, the fact that MRVSR outperforms RFS7 (+0.39dB in average PSNR and +0.0086 in average SSIM) shows that the temporal receptive field enabled by its contractive recurrence accounts for more than 7 frames. Recurrent models have gained popularity in deep learning (DL) based video super-resolution (VSR), due to their increased computational efficiency, temporal receptive field and temporal consistency compared to sliding-window based models. We demonstrate it on a new long sequence The video super-resolution (VSR) method based on the recurrent convolutional network has strong temporal modeling capability for video sequences. This work proposes a novel method that can effectively incorporate temporal information in a hierarchical way and achieves favorable performance against state-of-the-art methods on several benchmark datasets. When we capture a lot of sequential photos with a smartphone or handheld camera, there is always some movement present between the frames because of the hand motion. To accommo-date image processing, unlike regular LSTM cells, our LSTM module uses a convolution layer rather than fully connected ones. In the case of this network, we use the pre-trained weights available on its official github repository. While SISR aims to generate a high-resolution (HR) image from its low-(a) GT (b . Considering these points, we define a new framework of recurrent VSR network that is stable and performs competitively on long sequences: An Unconstrained Stable Recurrent VSR network is defined by an input network :[0,1]d(2T+1)Rd, a contractive recurrent network L:RnRdRn and an output network :RnRc. Benjamin Naoto Chiche, et al. Video surveillance is a typical example where such artifacts would occur, as both the camera and the scene stay static for a long time. Click To Get Model/Code. Read previous issues In this work, we expose instabilities of existing recurrent VSR networks on long sequences with low motion. Behavior analysis: These existing recurrent networks are trained to optimize their performance on a very low number of frames (at most 10). We additionally compare the reconstruction performances on the standard Vid4 dataset. These methods try to utilize some natural preferences and effectively estimate motion between frames. Firstly the low-resolution frame is transformed to the frequency domain. Therefore, it is crucial for video super-resolution (VSR) to fully utilize spatial and temporal information among video frames. Finally, MRVSR presents the best long-term reconstruction in terms of visual quality. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. The resolution of ground-truth frames is 19201080. We replace every vector multiplication in LSTM with This is because the resulting architecture has been constrained to be globally 1-Lipschitz, and a successful super-resolving functionthat operates both upsampling and deconvolutioncan not be 1-Lipschitz; since some frequencies need to be boosted as the Wiener filter does in the optimal linear case. Averaging or median aggregating are rather simple mathematical operations. is close to original Introduction Video super-resolution (VSR) is an inverse problem that extends single-image super-resolution (SISR). 3(d)). Other methods use wavelet transform, which helps to find similarities in neighboring local areas. Each track had more than 100 participants and 14 final results were submitted. The first two of them are respectively Full HD and HD Ready and the two others are 4K. After being better than the baseline RFS3 at the beginning of the sequences, it diverges (Fig. 1 (2.09dB in mean PSNR and 0.0284 in mean SSIM compared to RFS3 on the last 50 reconstructions). We empirically show its competitive performance on long sequences with low motion. The dataset consists of 9 videos, compressed with different video codec standards and different bitrates.
Emaar Community Management Contact Number, Matplotlib Plot Marker No Fill, National Poetry Week 2022, Which Country In South Asia Has Monarchy, Ut Southwestern Auto Interview, Types Of Interlocking Blocks, California Eligible Training Provider List, Gent Vigilon Commissioning Manual, Physics May June 2022 Threshold, Oxiclean Paste For Stains,
