50,671 research outputs found
CEL-Net: Continuous Exposure for Extreme Low-Light Imaging
Deep learning methods for enhancing dark images learn a mapping from input
images to output images with pre-determined discrete exposure levels. Often, at
inference time the input and optimal output exposure levels of the given image
are different from the seen ones during training. As a result the enhanced
image might suffer from visual distortions, such as low contrast or dark areas.
We address this issue by introducing a deep learning model that can
continuously generalize at inference time to unseen exposure levels without the
need to retrain the model. To this end, we introduce a dataset of 1500 raw
images captured in both outdoor and indoor scenes, with five different exposure
levels and various camera parameters. Using the dataset, we develop a model for
extreme low-light imaging that can continuously tune the input or output
exposure level of the image to an unseen one. We investigate the properties of
our model and validate its performance, showing promising results
Instance Segmentation in the Dark
Existing instance segmentation techniques are primarily tailored for
high-visibility inputs, but their performance significantly deteriorates in
extremely low-light environments. In this work, we take a deep look at instance
segmentation in the dark and introduce several techniques that substantially
boost the low-light inference accuracy. The proposed method is motivated by the
observation that noise in low-light images introduces high-frequency
disturbances to the feature maps of neural networks, thereby significantly
degrading performance. To suppress this ``feature noise", we propose a novel
learning method that relies on an adaptive weighted downsampling layer, a
smooth-oriented convolutional block, and disturbance suppression learning.
These components effectively reduce feature noise during downsampling and
convolution operations, enabling the model to learn disturbance-invariant
features. Furthermore, we discover that high-bit-depth RAW images can better
preserve richer scene information in low-light conditions compared to typical
camera sRGB outputs, thus supporting the use of RAW-input algorithms. Our
analysis indicates that high bit-depth can be critical for low-light instance
segmentation. To mitigate the scarcity of annotated RAW datasets, we leverage a
low-light RAW synthetic pipeline to generate realistic low-light data. In
addition, to facilitate further research in this direction, we capture a
real-world low-light instance segmentation dataset comprising over two thousand
paired low/normal-light images with instance-level pixel-wise annotations.
Remarkably, without any image preprocessing, we achieve satisfactory
performance on instance segmentation in very low light (4~\% AP higher than
state-of-the-art competitors), meanwhile opening new opportunities for future
research.Comment: Accepted by International Journal of Computer Vision (IJCV) 202
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