NTIRE 2024 challenge on bracketing image restoration and enhancement: Datasets methods and results
Low-light photography presents significant challenges. Multi-image processing methods
have made numerous attempts to obtain high-quality photos yet remain unsatisfactory …
have made numerous attempts to obtain high-quality photos yet remain unsatisfactory …
Nan: Noise-aware nerfs for burst-denoising
Burst denoising is now more relevant than ever, as computational photography helps
overcome sensitivity issues inherent in mobile phones and small cameras. A major …
overcome sensitivity issues inherent in mobile phones and small cameras. A major …
Image denoising in the deep learning era
S Izadi, D Sutton, G Hamarneh - Artificial Intelligence Review, 2023 - Springer
Over the last decade, the number of digital images captured per day has increased
exponentially, due to the accessibility of imaging devices. The visual quality of photographs …
exponentially, due to the accessibility of imaging devices. The visual quality of photographs …
A simple baseline for video restoration with grouped spatial-temporal shift
Video restoration, which aims to restore clear frames from degraded videos, has numerous
important applications. The key to video restoration depends on utilizing inter-frame …
important applications. The key to video restoration depends on utilizing inter-frame …
High quality reference feature for two stage bracketing image restoration and enhancement
X **ng, H Park, F Wang, Y Zhang… - Proceedings of the …, 2024 - openaccess.thecvf.com
In a low-light environment it is difficult to obtain high-quality or high-resolution images with
sharp details and high dynamic range (HDR) without noise or blur. To solve this problem the …
sharp details and high dynamic range (HDR) without noise or blur. To solve this problem the …
Unidirectional video denoising by mimicking backward recurrent modules with look-ahead forward ones
While significant progress has been made in deep video denoising, it remains very
challenging for exploiting historical and future frames. Bidirectional recurrent networks …
challenging for exploiting historical and future frames. Bidirectional recurrent networks …
Recurrent self-supervised video denoising with denser receptive field
Self-supervised video denoising has seen decent progress through the use of blind spot
networks. However, under their blind spot constraints, previous self-supervised video …
networks. However, under their blind spot constraints, previous self-supervised video …
Adaptive wavelet transformer network for 3d shape representation learning
We present a novel method for 3D shape representation learning using multi-scale wavelet
decomposition. Previous works often decompose 3D shapes into complementary …
decomposition. Previous works often decompose 3D shapes into complementary …
Rbsr: Efficient and flexible recurrent network for burst super-resolution
Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a
sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the …
sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the …
[PDF][PDF] Priors in Deep Image Restoration and Enhancement: A Survey
Image restoration and enhancement is a process of improving the image quality by
removing degradations, such as noise, blur, and resolution degradation. Deep learning (DL) …
removing degradations, such as noise, blur, and resolution degradation. Deep learning (DL) …