Fetching the paper…
Reading the bibliography…
Current deep learning methods for low-light image enhancement (LLIE) typically rely on pixel-wise mapping learned from paired data.
Scientific American
Land, E.H.: The retinex theory of color vision · 1977
Earlier work this paper cites.
IEEE Transactions on Consumer Electronics
Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization · 1997
Earlier work this paper cites.
IEEE Transactions on Image Processing
Stark, J.A.: Adaptive image contrast enhancement using generalizations of histogram equalization · 2000
Earlier work this paper cites.
International Journal of Computer Vision
Kimmel, R., Elad, M., Shaked, D., Keshet, R., Sobel, I.: A variational framework for retinex · 2003
Earlier work this paper cites.
Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology
Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement · 2004
Earlier work this paper cites.
IEEE Transactions on Image Processing
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity · 2004
Earlier work this paper cites.
IEEE Transactions on Image Processing
Huang, S.C., Cheng, F.C., Chiu, Y.S.: Efficient contrast enhancement using adaptive gamma correction with weighting distribution · 2012
Earlier work this paper cites.
In: Proceedings of International Conference on Image Processing, pp. 965–968 (2012)
Lee, C., Lee, C., Kim, C.S.: Contrast enhancement based on layered difference representation · 2012
Earlier work this paper cites.
IEEE Transactions on Image Processing
Lee, C., Lee, C., Kim, C.S.: Contrast enhancement based on layered difference representation of 2d histograms · 2013
Earlier work this paper cites.
IEEE Transactions on Image Processing
Wang, S., Zheng, J., Hu, H.M., Li, B.: Naturalness preserved enhancement algorithm for non-uniform illumination images · 2013
Earlier work this paper cites.
IEEE Transactions on Image Processing
Wang, L., Xiao, L., Liu, H., Wei, Z.: Variational bayesian method for retinex · 2014
Earlier work this paper cites.
In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification · 2015
Earlier work this paper cites.
In: Proceedings of International Conference on Learning Representations (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization 3rd int · 2015
Earlier work this paper cites.
IEEE Transactions on Image Processing
Ma, K., Zeng, K., Wang, Z.: Perceptual quality assessment for multi-exposure image fusion · 2015
Earlier work this paper cites.
In: Proceedings of International Conference on Machine Learning, pp. 2256–2265 (2015)
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics · 2015
Earlier work this paper cites.
IEEE Transactions on Image Processing
Zhang, L., Zhang, L., Bovik, A.C.: A feature-enriched completely blind image quality evaluator · 2015
Earlier work this paper cites.
In: Proceedings of International Conference on Learning Representations (2016)
Bruna, J., Sprechmann, P., LeCun, Y.: Super-resolution with deep convolutional sufficient statistics · 2016
Earlier work this paper cites.
Signal Processing
Fu, X., Zeng, D., Huang, Y., Liao, Y., Ding, X., Paisley, J.: A fusion-based enhancing method for weakly illuminated images · 2016
Earlier work this paper cites.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2782–2790 (2016)
Fu, X., Zeng, D., Huang, Y., Zhang, X.P., Ding, X.: A weighted variational model for simultaneous reflectance and illumination estimation · 2016
Earlier work this paper cites.
IEEE Transactions on Image Processing
Guo, X., Li, Y., Ling, H.: Lime: Low-light image enhancement via illumination map estimation · 2016
Earlier work this paper cites.
Pattern Recognition
Lore, K.G., Akintayo, A., Sarkar, S.: Llnet: A deep autoencoder approach to natural low-light image enhancement · 2017
Earlier work this paper cites.
arXiv preprint arXiv:1711.00591 (2017)
Ying, Z., Li, G., Gao, W.: A bio-inspired multi-exposure fusion framework for low-light image enhancement · 2017
Earlier work this paper cites.
In: Proceedings of the European Conference on Computer Vision Workshops (2018)
Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., Zelnik-Manor, L.: The 2018 pirm challenge on perceptual image super-resolution · 2018
Earlier work this paper cites.
IEEE Transactions on Image Processing
Cai, J., Gu, S., Zhang, L.: Learning a deep single image contrast enhancer from multi-exposure images · 2018
Earlier work this paper cites.
IEEE Transactions on Image Processing
Li, M., Liu, J., Yang, W., Sun, X., Guo, Z.: Structure-revealing low-light image enhancement via robust retinex model · 2018
Earlier work this paper cites.
In: Proceedings of International Conference on Machine learning, pp. 3481–3490 (2018)
Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for gans do actually converge? · 2018
Earlier work this paper cites.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2502–2510 (2018)
Mildenhall, B., Barron, J.T., Chen, J., Sharlet, D., Ng, R., Carroll, R.: Burst denoising with kernel prediction networks · 2018
Cited alongside, same era.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3118–3126 (2018)
Shocher, A., Cohen, N., Irani, M.: “zero-shot” super-resolution using deep internal learning · 2018
Cited alongside, same era.
In: Proceedings of the British Machine Vision Conference (2018)
Wei, C., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement · 2018
Cited alongside, same era.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3262–3271 (2018)
Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations · 2018
Cited alongside, same era.
In: Proceedings of the ACM International Conference on Multimedia, pp. 582–590 (2018)
Zhang, Q., Yuan, G., Xiao, C., Zhu, L., Zheng, W.S.: High-quality exposure correction of underexposed photos · 2018
In: Proceedings of IEEE International Conference on Computer Vision, pp. 14347–14356 (2021)
Choi, J., Kim, S., Jeong, Y., Gwon, Y., Yoon, S.: Ilvr: Conditioning method for denoising diffusion probabilistic models · 2021
Later among the works it cites.
IEEE Transactions on Image Processing
Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., Wang, Z.: Enlightengan: Deep light enhancement without paired supervision · 2021
Later among the works it cites.
International Journal of Computer Vision
Liu, J., Xu, D., Yang, W., Fan, M., Huang, H.: Benchmarking low-light image enhancement and beyond · 2021
Later among the works it cites.
In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1840–1844 (2021)
Liu, Y., Wang, Z., Zeng, Y., Zeng, H., Zhao, D.: Pd-gan: Perceptual-details gan for extremely noisy low light image enhancement · 2021
Later among the works it cites.
In: Proceedings of International Conference on Machine Learning, pp. 8162–8171 (2021)
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018)
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric · 2018
Cited alongside, same era.
In: Proceedings of Advances in Neural Information Processing Systems (2019)
Bell-Kligler, S., Shocher, A., Irani, M.: Blind super-resolution kernel estimation using an internal-gan · 2019
Cited alongside, same era.
In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3185–3194 (2019)
Chen, C., Chen, Q., Do, M.N., Koltun, V.: Seeing motion in the dark · 2019
Cited alongside, same era.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1712–1722 (2019)
Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs · 2019
Cited alongside, same era.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6849–6857 (2019)
Wang, R., Zhang, Q., Fu, C.W., Shen, X., Zheng, W.S., Jia, J.: Underexposed photo enhancement using deep illumination estimation · 2019
Cited alongside, same era.
In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1632–1640 (2019)
Zhang, Y., Zhang, J., Guo, X.: Kindling the darkness: A practical low-light image enhancer · 2019
Cited alongside, same era.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1780–1789 (2020)
Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., Cong, R.: Zero-reference deep curve estimation for low-light image enhancement · 2020
Cited alongside, same era.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2021)
Risheng, L., Long, M., Jiaao, Z., Xin, F., Zhongxuan, L.: Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement · 2021
Later among the works it cites.
IEEE Transactions on Image Processing
Yang, W., Wang, W., Huang, H., Wang, S., Liu, J.: Sparse gradient regularized deep retinex network for robust low-light image enhancement · 2021
Later among the works it cites.
International Journal of Computer Vision
Zhang, Y., Guo, X., Ma, J., Liu, W., Zhang, J.: Beyond brightening low-light images · 2021
Later among the works it cites.
arXiv preprint arXiv:2212.04711 (2022)
Guo, L., Wang, C., Yang, W., Huang, S., Wang, Y., Pfister, H., Wen, B.: Shadowdiffusion: When degradation prior meets diffusion model for shadow removal · 2022
Later among the works it cites.
ACM Transactions on Multimedia Computing, Communications, and Applications
Hao, S., Han, X., Guo, Y., Wang, M.: Decoupled low-light image enhancement · 2022
Later among the works it cites.
Journal of Machine Learning Research
Ho, J., Saharia, C., Chan, W., Fleet, D.J., Norouzi, M., Salimans, T.: Cascaded diffusion models for high fidelity image generation · 2022
Later among the works it cites.
In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1078–1086 (2022)
Jiang, K., Wang, Z., Wang, Z., Chen, C., Yi, P., Lu, T., Lin, C.W.: Degrade is upgrade: Learning degradation for low-light image enhancement · 2022
Later among the works it cites.
In: Proceedings of European Conference on Computer Vision, pp. 736–753 (2022)
Li, D., Zhang, Y., Cheung, K.C., Wang, X., Qin, H., Li, H.: Learning degradation representations for image deblurring · 2022
Later among the works it cites.
International Journal of Computer Vision
Li, D., Zhang, Y., Law, K.L., Wang, X., Qin, H., Li, H.: Efficient burst raw denoising with variance stabilization and multi-frequency denoising network · 2022
Later among the works it cites.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11461–11471 (2022)
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Van Gool, L.: Repaint: Inpainting using denoising diffusion probabilistic models · 2022
Later among the works it cites.
arXiv preprint arXiv:2207.14626 (2022)
Özdenizci, O., Legenstein, R.: Restoring vision in adverse weather conditions with patch-based denoising diffusion models · 2022
Later among the works it cites.
arXiv preprint arXiv:2212.01789 (2022)
Ren, M., Delbracio, M., Talebi, H., Gerig, G., Milanfar, P.: Image deblurring with domain generalizable diffusion models · 2022
Later among the works it cites.
In: Proceedings of ACM SIGGRAPH Conference Proceedings, pp. 1–10 (2022)
Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., Norouzi, M.: Palette: Image-to-image diffusion models · 2022
Later among the works it cites.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement · 2022
Later among the works it cites.
In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2604–2612 (2022)
Wang, Y., Wan, R., Yang, W., Li, H., Chau, L.P., Kot, A.: Low-light image enhancement with normalizing flow · 2022
Later among the works it cites.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 17683–17693 (2022)
Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: A general u-shaped transformer for image restoration · 2022
Later among the works it cites.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 16293–16303 (2022)
Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A.G., Milanfar, P.: Deblurring via stochastic refinement · 2022
Later among the works it cites.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5728–5739 (2022)
Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: Efficient transformer for high-resolution image restoration · 2022
Later among the works it cites.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)
Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., Shao, L.: Learning enriched features for fast image restoration and enhancement · 2022
Later among the works it cites.
In: Proceedings of the AAAI Conference on Artificial Intelligence (2023)
Wang, T., Zhang, K., Shen, T., Luo, W., Stenger, B., Lu, T.: Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method · 2023
Closest in time.