Dual Illumination Estimation for Robust Exposure Correction

@article{Zhang2019DualIE,
  title={Dual Illumination Estimation for Robust Exposure Correction},
  author={Qing Zhang and Yongwei Nie and Weishi Zheng},
  journal={Computer Graphics Forum},
  year={2019},
  volume={38}
}
Exposure correction is one of the fundamental tasks in image processing and computational photography. While various methods have been proposed, they either fail to produce visually pleasing results, or only work well for limited types of image (e.g., underexposed images). In this paper, we present a novel automatic exposure correction method, which is able to robustly produce high‐quality results for images of various exposure conditions (e.g., underexposed, overexposed, and partially under… 
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References

SHOWING 1-10 OF 44 REFERENCES
High-Quality Exposure Correction of Underexposed Photos
TLDR
This work addresses the problem of correcting the exposure of underexposed photos by casting the exposure correction problem as an illumination estimation optimization, where PBS is defined as three constraints for estimating illumination that can generate the desired result with even exposure, vivid color and clear textures.
Correcting over-exposure in photographs
TLDR
This paper introduces a method to correct over-exposure in an existing photograph by recovering the color and lightness separately, which is fully automatic and requires only one single input photo.
LIME: Low-Light Image Enhancement via Illumination Map Estimation
TLDR
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
TLDR
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.
A low-light image enhancement method for both denoising and contrast enlarging
TLDR
A novel united low-light image enhancement framework for both contrast enhancement and denoising is proposed and outperforms traditional methods in both subjective and objective assessments.
Recovering Over-/Underexposed Regions in Photographs
TLDR
A wavelet tight frame--based approach to reconstruct a well-exposed image with better visibility of details than that with over-/underexposed regions, and it performs better than other existing methods on tested real photographs.
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images
TLDR
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.
Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images
TLDR
Experimental results demonstrate that the proposed enhancement algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.
Automatic Exposure Correction of Consumer Photographs
  • Lu Yuan, Jian Sun
  • Computer Science
    ECCV
  • 2012
TLDR
This paper will automate the interactive correction technique by estimating the image specific S-shaped non-linear tone curve that best fits the input image by creating a new Zone-based region-level optimal exposure evaluation, which would consider both the visibility of individual regions and relative contrast between regions.
...
...