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Found 32 papers, 15 papers with code
Face Deblurring using Dual Camera Fusion on Mobile Phones
To the best of our knowledge, our work is the first mobile solution for face motion deblurring that works reliably and robustly over thousands of images in diverse motion and lighting conditions.
Vision Transformer for NeRF-Based View Synthesis from a Single Input Image
Existing approaches condition on local image features to reconstruct a 3D object, but often render blurry predictions at viewpoints that are far away from the source view.
Deep Image Deblurring: A Survey
Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image.
Correcting Face Distortion in Wide-Angle Videos
Video blogs and selfies are popular social media formats, which are often captured by wide-angle cameras to show human subjects and expanded background.
Toward Real-World Super-Resolution via Adaptive Downsampling Models
Most image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs that are constructed by a predetermined operation, e. g., bicubic downsampling.
Stylizing 3D Scene via Implicit Representation and HyperNetwork
Our framework consists of two components: an implicit representation of the 3D scene with the neural radiance fields model, and a hypernetwork to transfer the style information into the scene representation.
Hybrid Neural Fusion for Full-frame Video Stabilization
Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views.
Deep Online Fused Video Stabilization
We present a deep neural network (DNN) that uses both sensor data (gyroscope) and image content (optical flow) to stabilize videos through unsupervised learning.
Dual-Stream Fusion Network for Spatiotemporal Video Super-Resolution
Our method is based on an important observation that: even the direct cascade of prior research in spatial and temporal super-resolution can achieve the spatiotemporal upsampling, changing orders for combining them would lead to results with a complementary property.
Portrait Neural Radiance Fields from a Single Image
We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait.
Real-time Localized Photorealistic Video Style Transfer
We present a novel algorithm for transferring artistic styles of semantically meaningful local regions of an image onto local regions of a target video while preserving its photorealism.
Learning to See Through Obstructions with Layered Decomposition
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captured by a moving camera.
Learning to See Through Obstructions
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization.
Gated Fusion Network for Degraded Image Super Resolution
To address this problem, we propose a dual-branch convolutional neural network to extract base features and recovered features separately.
Exploiting Semantics for Face Image Deblurring
Specifically, we first use a coarse deblurring network to reduce the motion blur on the input face image.
Visual Question Answering on 360° Images
We then study two different VQA models on VQA 360, including one conventional model that takes an equirectangular image (with intrinsic distortion) as input and one dedicated model that first projects a 360 image onto cubemaps and subsequently aggregates the information from multiple spatial resolutions.
Video Stitching for Linear Camera Arrays
Despite the long history of image and video stitching research, existing academic and commercial solutions still produce strong artifacts.
Depth-Aware Video Frame Interpolation
The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame.
Ranked #5 on
Video Frame Interpolation
on Middlebury
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement
Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed.
Ranked #21 on
Video Frame Interpolation
on Vimeo90K
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and Enhancement
In this work, we propose a motion estimation and motion compensation driven neural network for video frame interpolation.
Ranked #6 on
Video Frame Interpolation
on Middlebury
Learning Blind Video Temporal Consistency
Our method takes the original unprocessed and per-frame processed videos as inputs to produce a temporally consistent video.
Gated Fusion Network for Joint Image Deblurring and Super-Resolution
Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution.
Learning a Discriminative Prior for Blind Image Deblurring
We present an effective blind image deblurring method based on a data-driven discriminative prior. Our work is motivated by the fact that a good image prior should favor clear images over blurred images. In this work, we formulate the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN). The learned prior is able to distinguish whether an input image is clear or not. Embedded into the maximum a posterior (MAP) framework, it helps blind deblurring in various scenarios, including natural, face, text, and low-illumination images. However, it is difficult to optimize the deblurring method with the learned image prior as it involves a non-linear CNN. Therefore, we develop an efficient numerical approach based on the half-quadratic splitting method and gradient decent algorithm to solve the proposed model. Furthermore, the proposed model can be easily extended to non-uniform deblurring. Both qualitative and quantitative experimental results show that our method performs favorably against state-of-the-art algorithms as well as domain-specific image deblurring approaches.
Deep Semantic Face Deblurring
In this paper, we present an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks (CNNs).
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks
Convolutional neural networks (CNNs) have recently been applied to the optical flow estimation problem.
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
However, existing methods often require a large number of network parameters and entail heavy computational loads at runtime for generating high-accuracy super-resolution results.
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution.
Ranked #36 on
Image Super-Resolution
on Urban100 - 4x upscaling
Semantic-driven Generation of Hyperlapse from $360^\circ$ Video
We present a system for converting a fully panoramic ($360^\circ$) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience.
Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution
In this paper, we propose a fully convolutional networks for iterative non-blind deconvolution We decompose the non-blind deconvolution problem into image denoising and image deconvolution.
A Comparative Study for Single Image Blind Deblurring
Using these datasets, we conduct a large-scale user study to quantify the performance of several representative state-of-the-art blind deblurring algorithms.
Blur Kernel Estimation Using Normalized Color-Line Prior
The intermediate patches can then guide the estimation of the blur kernel.
