Ieee Transactions on Pattern Analysis and Machine Intelligence 1 on Bayesian Adaptive Video Super Resolution

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (2021, methodology): “5×(s− 1) pixels via a 2D linear grid interpolation method [25], and apply it for convolution before the nearest neighbour downsampling.”
Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation (2018, methodology): “Traditional VSR (or multi-image super-resolution) algorithms take multiple LR frames as inputs and output HR frames by taking into account subpixel motions between the neighboring LR frames [28, 4, 23, 26].”
Deep Unfolding Network for Image Super-Resolution (2020, methodology): “Note that following [37], we ﬁx the pixel shift issue before calculating PSNR if necessary.”
EDVR: Video Restoration With Enhanced Deformable Convolutional Networks (2019, methodology): “We compare our EDVR method with nine algorithms: RCAN [51], DeepSR [19], BayesSR [21], VESPCN [2], SPMC [37], TOFlow [48], FRVSR [32], DUF [10] and RBPN [6] on three testing datasets: Vid4 [21], Vimeo-90K-T [48] and REDS4.”
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data (2021, methodology): “Classical degradation model [10, 28] is widely adopted in blind SR methods [54, 15, 33].”
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment (2021, methodology): “BI degradation BD degradation Params (M) Runtime (ms) REDS4 [21] Vimeo-90K-T [35] Vid4 [19] UDM10 [37] Vimeo-90K-T [35] Vid4 [19] Bicubic - 26.”
Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations (2019, methodology): “Most traditional video SR methods [2, 7, 20, 22] conduct pixel-level motion and blur kernel estimation based on image priors, trying to solve a complex optimization problem [23].”
Image super-resolution: The techniques, applications, and future (2016, methodology): “It has to be mentioned that the Bayesian framework [53,72] is also a powerful tool for modeling unknown parameters, including the regularization parameter, the blur kernel, and motion vectors.”
Flow-based Kernel Prior with Application to Blind Super-Resolution (2021, methodology): “Generally, the classical degradation model of image SR [12, 13, 31] assumes the LR image y is obtained via a composition of blurring and downsampling from the HR image x.”