# KinD_plus **Repository Path**: xxxxcp/KinD_plus ## Basic Information - **Project Name**: KinD_plus - **Description**: Beyond Brightening Low-light Images - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2020-12-04 - **Last Updated**: 2021-11-17 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # KinD++ This is a Tensorflow implementation of KinD++. (Beyond Brightening Low-light Images) We propose a novel multi-scale illumination attention module (MSIA), which can alleviate visual defects (e.g. non-uniform spots and over-smoothing) left in [KinD](https://github.com/zhangyhuaee/KinD). The KinD net was proposed in the following [Paper](http://doi.acm.org/10.1145/3343031.3350926). Kindling the Darkness: a Practical Low-light Image Enhancer. In ACM MM 2019
Yonghua Zhang, Jiawan Zhang, Xiaojie Guo **** ## The network architecture of KinD++: ##

---- ## The reflectance restoration network and the MSIA module: ##

_____ ### Visual comparison with state-of-the-art low-light image enhancement methods. ###

---- ## Requirements ## 1. Python 2. Tensorflow >= 1.10.0 3. numpy, PIL ## Test ## Please put test images into 'test_images' folder and download the pre-trained checkpoints from [google drive](https://drive.google.com/open?id=1RuW6fgkDEQ6v9GMlcWgtWiGglew6jplO) or [BaiduNetDisk](https://pan.baidu.com/s/1DY49dJSlue1PNWy05nZb5A), then just run ```shell python evaluate.py ``` The test datasets (e.g. DICM, LIME, MEF and NPE) can be downloaded from [google drive](https://drive.google.com/open?id=12sUp8aOlNIB5h11lwsjs1Qm9sdH7v5p1). Our enhancement results of these datasets can be downloaded from [google drive](https://drive.google.com/open?id=1dBLdVV_-DEPyGOT5W8aLOetBqtEaMcl6). ## Train ## The original LOLdataset can be downloaded from [here](https://daooshee.github.io/BMVC2018website/). We rearrange the original LOLdataset and add several pairs all-zero images and 260 pairs synthetic images to improve the decomposition results and restoration results. The training dataset can be downloaded from [google drive](https://drive.google.com/open?id=1YztDWbK3MV5EroSpuWmYlPsmFcFGoLmq). For training, just run ```shell python decomposition_net_train.py python illumination_adjustment_net_train.py python reflectance_restoration_net_train.py ``` You can also evaluate on the LOLdataset, just run ```shell python evaluate_LOLdataset.py ``` ## A survey of low-light image enhancement methods ## ### Traditional methods: ### 1. Single-scale Retinex (SSR) [5] 2. Multi-scale Retinex (MSR) [6] 3. Naturalness preserved enhancement (NPE) [7] 4. Fusion-based enhancing method (MEF) [8] 5. LIME [2] 6. SRIE [9] 7. Dong [10] 8. BIMEF [11] The __codes__ of above-mentioned methods can be found from [here](https://github.com/baidut/BIMEF/tree/master/lowlight). 9. CRM [12] ([code](https://github.com/zhangyhuaee/KinD_plus/tree/master/CRM)) ### Deep learning methods: ### 10. RetinexNet [3] ([code](https://github.com/weichen582/RetinexNet)) 11. GLADNet [13] ([code](https://github.com/weichen582/GLADNet)) 12. DeepUPE [4] ([code](https://github.com/wangruixing/DeepUPE)) 13. KinD [1] ([code](https://github.com/zhangyhuaee/KinD)) ## NIQE code ## Non-reference metric NIQE is adopted for quantitative comparison. The original code for computing NIQE is [here](https://github.com/csjunxu/Bovik_NIQE_SPL2013). To improve the robustness, we follow the author's code and retrain the model parameters by extending 100 high-resolution natural images from [PIRM dataset](https://pirm.github.io/). Put the [original 125 images](http://live.ece.utexas.edu/research/quality/pristinedata.zip) and additional 100 images (dir: PIRM_dataset\Validation\Original) into one folder 'data', then run ```shell [mu_prisparam cov_prisparam] = estimatemodelparam('data',96,96,0,0,0.75); ``` After retrained, the file 'modelparameters_new.mat' will be generated. We use this model to evaluate all results. ## References ## [1] Y. Zhang, J. Zhang, and X. Guo, “Kindling the darkness: A practical low-light image enhancer,” in ACM MM, 2019, pp. 1632–1640. [2] X. Guo, Y. Li, and H. Ling, “Lime: Low-light image enhancement via illumination map estimation,” IEEE TIP, vol. 26, no. 2, pp. 982–993, 2017. [3] C. Wei, W. Wang, W. Yang, and J. Liu, “Deep retinex decomposition for low-light enhancement,” in BMVC, 2018. [4] R. Wang, Q. Zhang, C.-W. Fu, X. Shen, W.-S. Zheng, and J. Jia, “Underexposed photo enhancement using deep illumination estimation,” in CVPR, 2019, pp. 6849–6857. [5] D. J. Jobson, Z. Rahman, and G. A. Woodell, “Properties and performance of a center/surround retinex,” IEEE TIP, vol. 6, no. 3, pp. 451–462, 1997. [6] D. J. Jobson, Z. Rahman, and G. A. Woodell, “A multiscale retinex for bridging the gap between color images and the human observation of scenes,” IEEE TIP, vol. 6, no. 7, pp. 965–976, 2002. [7] S. Wang, J. Zheng, H. Hu, and B. Li, “Naturalness preserved enhancement algorithm for non-uniform illumination images,” IEEE TIP, vol. 22, no. 9, pp. 3538–3548, 2013. [8] X. Fu, D. Zeng, H. Yue, Y. Liao, X. Ding, and J. Paisley, “A fusion-based enhancing method for weakly illuminated images,” Signal Processing, vol. 129, pp. 82–96, 2016. [9] X. Fu, D. Zeng, Y. Huang, X. Zhang, and X. Ding, “A weighted variational model for simultaneous reﬂectance and illumination estimation,” in CVPR, 2016, pp. 2782–2790. [10] X. Dong, Y. Pang, and J. Wen, “Fast efﬁcient algorithm for enhancement of low lighting video,” in ICME, 2011, pp. 1–6. [11] Z. Ying, L. Ge, and W. Gao, “A bio-inspired multi-exposure fusion framework for low-light image enhancement,” arXiv: 1711.00591, 2017. [12] Z. Ying, L. Ge, Y. Ren, R. Wang, and W. Wang, “A new low-light image enhancement algorithm using camera response model,” in ICCVW, 2018, pp. 3015–3022. [13] W. Wang, W. Chen, W. Yang, and J. Liu, “Gladnet: Low-light enhancement network with global awareness,” in FG, 2018.