Artificial intelligence in the creative industries: a review
N Anantrasirichai, D Bull - Artificial intelligence review, 2022 - Springer
This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …
applications in the context of the creative industries. A brief background of AI, and …
On the use of deep learning for computational imaging
Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …
and machine learning have followed parallel tracks and, during the last two decades …
Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement
Retinex model-based methods have shown to be effective in layer-wise manipulation with
well-designed priors for low-light image enhancement. However, the commonly used hand …
well-designed priors for low-light image enhancement. However, the commonly used hand …
SNR-aware low-light image enhancement
This paper presents a new solution for low-light image enhancement by collectively
exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to …
exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to …
Retinexformer: One-stage retinex-based transformer for low-light image enhancement
When enhancing low-light images, many deep learning algorithms are based on the Retinex
theory. However, the Retinex model does not consider the corruptions hidden in the dark or …
theory. However, the Retinex model does not consider the corruptions hidden in the dark or …
Nerf in the dark: High dynamic range view synthesis from noisy raw images
B Mildenhall, P Hedman… - Proceedings of the …, 2022 - openaccess.thecvf.com
Abstract Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis
from a collection of posed input images. Like most view synthesis methods, NeRF uses …
from a collection of posed input images. Like most view synthesis methods, NeRF uses …
Towards universal fake image detectors that generalize across generative models
With generative models proliferating at a rapid rate, there is a growing need for general
purpose fake image detectors. In this work, we first show that the existing paradigm, which …
purpose fake image detectors. In this work, we first show that the existing paradigm, which …
Learning enriched features for fast image restoration and enhancement
Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …
image content. Numerous applications demand effective image restoration, eg …
Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method
As the quality of optical sensors improves, there is a need for processing large-scale
images. In particular, the ability of devices to capture ultra-high definition (UHD) images and …
images. In particular, the ability of devices to capture ultra-high definition (UHD) images and …
Low-light image enhancement with normalizing flow
To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the
mapping relationship between them is one-to-many. Previous works based on the pixel-wise …
mapping relationship between them is one-to-many. Previous works based on the pixel-wise …