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NTIRE 2021 challenge on image deblurring
Motion blur is a common photography artifact in dynamic environments that typically comes
jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on …
jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on …
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. Advances in deep learning have led to significant …
sharp image from a blurred input image. Advances in deep learning have led to significant …
Real-world blur dataset for learning and benchmarking deblurring algorithms
Numerous learning-based approaches to single image deblurring for camera and object
motion blurs have recently been proposed. To generalize such approaches to real-world …
motion blurs have recently been proposed. To generalize such approaches to real-world …
Lednet: Joint low-light enhancement and deblurring in the dark
S Zhou, C Li, C Change Loy - European conference on computer vision, 2022 - Springer
Night photography typically suffers from both low light and blurring issues due to the dim
environment and the common use of long exposure. While existing light enhancement and …
environment and the common use of long exposure. While existing light enhancement and …
Spatio-temporal filter adaptive network for video deblurring
Video deblurring is a challenging task due to the spatially variant blur caused by camera
shake, object motions, and depth variations, etc. Existing methods usually estimate optical …
shake, object motions, and depth variations, etc. Existing methods usually estimate optical …
Spatial-adaptive network for single image denoising
M Chang, Q Li, H Feng, Z Xu - European conference on computer vision, 2020 - Springer
Previous works have shown that convolutional neural networks can achieve good
performance in image denoising tasks. However, limited by the local rigid convolutional …
performance in image denoising tasks. However, limited by the local rigid convolutional …
Parallax attention for unsupervised stereo correspondence learning
Stereo image pairs encode 3D scene cues into stereo correspondences between the left
and right images. To exploit 3D cues within stereo images, recent CNN based methods …
and right images. To exploit 3D cues within stereo images, recent CNN based methods …
Realistic blur synthesis for learning image deblurring
Training learning-based deblurring methods demands a tre-mendous amount of blurred and
sharp image pairs. Unfortunately, existing synthetic datasets are not realistic enough, and …
sharp image pairs. Unfortunately, existing synthetic datasets are not realistic enough, and …
Ntire 2020 challenge on image and video deblurring
Motion blur is one of the most common degradation artifacts in dynamic scene photography.
This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this …
This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this …
Beyond monocular deraining: Parallel stereo deraining network via semantic prior
Rain is a common natural phenomenon. Taking images in the rain however often results in
degraded quality of images, thus compromises the performance of many computer vision …
degraded quality of images, thus compromises the performance of many computer vision …