NTIRE 2024 Challenge on Night Photography Rendering
Egor Ershov, Artyom Panshin, Oleg Karasev, Sergey Korchagin, Shepelev, Lev, Alexandr Startsev, Daniil Vladimirov, Ekaterina Zaychenkova, Nikola, Bani\'c, Dmitrii Iarchuk, Maria Efimova, Radu Timofte, Arseniy Terekhin,, Shuwei Yue, Yuyang Liu, Minchen Wei, Lu Xu, Chao Zhang

TL;DR
The NTIRE 2024 challenge on night photography rendering aimed to develop algorithms that convert raw nighttime images into high-quality sRGB images efficiently, evaluated through subjective quality assessments and processing speed.
Contribution
This challenge introduces a new benchmark for night photography rendering using mobile phone images, emphasizing both image quality and processing efficiency.
Findings
Top solutions achieve state-of-the-art quality in night image rendering.
Algorithms are evaluated on both visual quality and processing speed.
Mobile phone images are now effectively processed for high-quality night photography.
Abstract
This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year's competition, the challenge images were collected with a mobile phone and the speed of algorithms was also measured alongside the quality of their output. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. There were 2 nominations: quality and efficiency. Top 5 solutions in terms of output quality were sorted by evaluation time (see Fig. 1). The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering. More…
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Taxonomy
TopicsImpact of Light on Environment and Health · Computer Graphics and Visualization Techniques · Image Enhancement Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
