• Corpus ID: 251468304

Continuous Exposure for Extreme Low-Light Imaging

@inproceedings{Neiterman2020ContinuousEF,
  title={Continuous Exposure for Extreme Low-Light Imaging},
  author={Evgeny Hershkovitch Neiterman and Michael Klyuchka and Gil Ben-Artzi},
  year={2020}
}
We consider the problem of enhancing an underexposed dark image captured in a very low-light environment where details cannot be detected. Existing methods learn to adjust the input image’s exposure to a predetermined value. In practice, however, the optimal enhanced exposure varies from one input image to another, and as a result, the enhanced images may contain visual artifacts such as low-contrast or dark areas. We address this limitation by intro-ducing a deep learning model that allows the… 
[PDF] Semantic Reader

References

SHOWING 1-10 OF 36 REFERENCES

Learning to See in the Dark

A pipeline for processing low-light images is developed, based on end-to-end training of a fully-convolutional network that operates directly on raw sensor data and replaces much of the traditional image processing pipeline, which tends to perform poorly on such data.

Learning Multi-Scale Photo Exposure Correction

This work proposes a coarse- to-fine deep neural network model, trainable in an end-to-end manner, that addresses each sub-problem separately of the exposure correction problem as two main sub-problems: color enhancement and detail enhancement.

LIME: Low-Light Image Enhancement via Illumination Map Estimation

Experiments on a number of challenging low-light images are present to reveal the efficacy of the proposed LIME and show its superiority over several state-of-the-arts in terms of enhancement quality and efficiency.

Underexposed Photo Enhancement Using Deep Illumination Estimation

A new neural network for enhancing underexposed photos is presented, which introduces intermediate illumination in its network to associate the input with expected enhancement result, which augments the network's capability to learn complex photographic adjustment from expert-retouched input/output image pairs.

Kindling the Darkness: A Practical Low-light Image Enhancer

This work builds a simple yet effective network for Kindling the Darkness (denoted as KinD), which, inspired by Retinex theory, decomposes images into two components that are robust against severe visual defects, and user-friendly to arbitrarily adjust light levels.

Learning to Restore Low-Light Images via Decomposition-and-Enhancement

A frequency-based decompositionand- enhancement model that first learns to recover image objects in the low-frequency layer and then enhances high-frequency details based on the recovered image objects and outperforms state-of-the-art approaches in enhancing practical noisy low-light images.

Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images

This paper proposes to use the convolutional neural network (CNN) to train a SICE enhancer, and builds a large-scale multi-exposure image data set, which contains 589 elaborately selected high-resolution multi-Exposure sequences with 4,413 images.

Deblurring Low-Light Images with Light Streaks

This work introduces a non-linear blur model that explicitly models light streaks and their underlying light sources, and poses them as constraints for estimating the blur kernel in an optimization framework, and automatically detects useful light streaks in the input image.

Deep Retinex Decomposition for Low-Light Enhancement

Extensive experiments demonstrate that the proposed deep Retinex-Net learned on this LOw-Light dataset not only achieves visually pleasing quality for low-light enhancement but also provides a good representation of image decomposition.

Deep Convolutional Denoising of Low-Light Images

This paper demonstrates how by training the same network with images having a specific peak value, the denoiser outperforms previous state-of-the-art by a large margin both visually and quantitatively.