Update readme

Update paper information.

README.md

@@ -2,14 +2,14 @@
 *[Mahmoud Afifi](https://sites.google.com/view/mafifi)*, *[Abdelrahman Abdelhamed](https://www.eecs.yorku.ca/~kamel/)*, *[Abdullah Abuolaim](https://sites.google.com/view/abdullah-abuolaim/)*, *[Abhijith Punnappurath](https://abhijithpunnappurath.github.io/)*, and  *[Michael S. Brown](http://www.cse.yorku.ca/~mbrown/)*
 <br></br>York University
 
-Reference code for the paper [CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks](https://arxiv.org/pdf/2006.12709.pdf). Mahmoud Afifi, Abdelrahman Abdelhamed, Abdullah Abuolaim, Abhijith Punnappurath, and Michael S. Brown, arXiv preprint, 2020. If you use this code or our dataset, please cite our paper:
+Reference code for the paper [CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks](https://arxiv.org/pdf/2006.12709.pdf). Mahmoud Afifi, Abdelrahman Abdelhamed, Abdullah Abuolaim, Abhijith Punnappurath, and Michael S. Brown, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021. If you use this code or our dataset, please cite our paper:
 ```
 @article{CIEXYZNet,
   title={CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks},
   author={Afifi, Mahmoud and Abdelhamed, Abdelrahman and Abuolaim, Abdullah and Punnappurath, Abhijith and Brown, Michael S},
-  journal={arXiv preprint},
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
   pages={},
-  year={2020}
+  year={2021}
 }
 ```
 
@@ -87,24 +87,3 @@ Testing set (2.83 GB): [Part 0](https://ln2.sync.com/dl/bb19d1b90/nv38zdmq-n4b46
 ### Dataset License:
 As the dataset was originally rendered using raw images taken from the [MIT-Adobe FiveK dataset](https://data.csail.mit.edu/graphics/fivek/), our sRGB2XYZ dataset follows the original license of the [MIT-Adobe FiveK dataset](https://data.csail.mit.edu/graphics/fivek/).
 
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-### Related Research Projects
-- Image Enhancement:
-  - [Exposure Correction](https://github.com/mahmoudnafifi/Exposure_Correction): A coarse-to-fine deep learning model with adversarial training to correct badly-exposed photographs (CVPR 2021).
-- sRGB Image White Balancing:
-  - [When Color Constancy Goes Wrong](https://github.com/mahmoudnafifi/WB_sRGB): The first work for white-balancing camera-rendered sRGB images (CVPR 2019).
-  - [White-Balance Augmenter](https://github.com/mahmoudnafifi/WB_color_augmenter): Emulating white-balance effects for color augmentation; it improves the accuracy of image classification and image semantic segmentation methods (ICCV 2019).
-  - [Color Temperature Tuning](https://github.com/mahmoudnafifi/ColorTempTuning): A camera pipeline that allows accurate post-capture white-balance editing (CIC best paper award, 2019).
-  - [Interactive White Balancing](https://github.com/mahmoudnafifi/Interactive_WB_correction): Interactive sRGB image white balancing using polynomial correction mapping (CIC 2020).
-  - [Deep White-Balance Editing](https://github.com/mahmoudnafifi/Deep_White_Balance): A multi-task deep learning model for post-capture white-balance editing (CVPR 2020).
-- Raw Image White Balancing:
-  - [APAP Bias Correction](https://github.com/mahmoudnafifi/APAP-bias-correction-for-illumination-estimation-methods): A locally adaptive bias correction technique for illuminant estimation (JOSA A 2019).
-  - [SIIE](https://github.com/mahmoudnafifi/SIIE): A sensor-independent deep learning framework for illumination estimation (BMVC 2019).
-  - [C5](https://github.com/mahmoudnafifi/C5): A self-calibration method for cross-camera illuminant estimation (arXiv 2020).
- - Image Manipulation:
-    - [MPB](https://github.com/mahmoudnafifi/modified-Poisson-image-editing): Image blending using a two-stage Poisson blending (CVM 2016).
-    - [Image Recoloring](https://github.com/mahmoudnafifi/Image_recoloring): A fully automated image recoloring with no target/reference images (Eurographics 2019).
-    - [Image Relighting](https://github.com/mahmoudnafifi/image_relighting): Relighting using a uniformly-lit white-balanced version of input images (Runner-Up Award overall tracks of AIM 2020 challenge for image relighting, ECCV Workshops 2020). 
-    - [HistoGAN](https://github.com/mahmoudnafifi/HistoGAN): Controlling colors of GAN-generated images based on features derived directly from color histograms (CVPR 2021).