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The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)In this paper, we tackle the problem of convolutional neural network design. Instead of focusing on the design of the overall architecture, we investigate a design space that is usually overlooked, i.e. adjusting the channel configurations of predefined networks. We find that this adjustment can be achieved by shrinking widened baseline networks and leads to superior performance. Based on that, we articulate the "heterogeneity hypothesis": ...Conference Paper -
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. Although several degradation models take additional factors into consideration, such as blur, they are still not effective enough to cover the diverse degradations of real images. To address this issue, this paper proposes to design a more complex but practical degradation ...Conference Paper -
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially invariant across the whole image. However, such an assumption is rarely applicable for real images whose blur kernels are usually spatially variant due to factors such as object motion and out-of-focus. Hence, existing blind SR methods would inevitably give rise to poor performance in real applications. To address this issue, this paper proposes a ...Conference Paper -
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Normalizing flows have recently demonstrated promising results for low-level vision tasks. For image super-resolution (SR), it learns to predict diverse photo-realistic high-resolution (HR) images from the low-resolution (LR) image rather than learning a deterministic mapping. For image rescaling, it achieves high accuracy by jointly modelling the downscaling and upscaling processes. While existing approaches employ specialized techniques ...Conference Paper -
Towards Interpretable Video Super-Resolution via Alternating Optimization
(2022)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2022In this paper, we study a practical space-time video super-resolution (STVSR) problem which aims at generating a high-framerate high-resolution sharp video from a low-framerate low-resolution blurry video. Such problem often occurs when recording a fast dynamic event with a low-framerate and low-resolution camera, and the captured video would suffer from three typical issues: i) motion blur occurs due to object/camera motions during ...Conference Paper -
Reference-Based Image Super-Resolution with Deformable Attention Transformer
(2022)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2022Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref) images to super-resolve low-resolution (LR) images. Recently, RefSR has been attracting great attention as it provides an alternative way to surpass single image SR. However, addressing the RefSR problem has two critical challenges: (i) It is difficult to match the correspondence between LR and Ref images when they are significantly different; (ii) ...Conference Paper -
Deep Unfolding Network for Image Super-Resolution
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Learning-based single image super-resolution (SISR) methods are continuously showing superior effectiveness and efficiency over traditional model-based methods, largely due to the end-to-end training. However, different from model-based methods that can handle the SISR problem with different scale factors, blur kernels and noise levels under a unified MAP (maximum a posteriori) framework, learning-based methods generally lack such ...Conference Paper -
Flow-based Kernel Prior with Application to Blind Super-Resolution
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Kernel estimation is generally one of the key problems for blind image super-resolution (SR). Recently, Double-DIP proposes to model the kernel via a network architecture prior, while KernelGAN employs the deep linear network and several regularization losses to constrain the kernel space. However, they fail to fully exploit the general SR kernel assumption that anisotropic Gaussian kernels are sufficient for image SR. To address this ...Conference Paper -
DHP: Differentiable Meta Pruning via HyperNetworks
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Network pruning has been the driving force for the acceleration of neural networks and the alleviation of model storage/transmission burden. With the advent of AutoML and neural architecture search (NAS), pruning has become topical with automatic mechanism and searching based architecture optimization. Yet, current automatic designs rely on either reinforcement learning or evolutionary algorithm. Due to the non-differentiability of those ...Conference Paper -
SwinIR: Image Restoration Using Swin Transformer
(2021)2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW 2021)Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we propose a strong baseline model SwinIR for image ...Conference Paper