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AI Image SIgnal Processing and Computational Photography - Bokeh Rendering , Reversed ISP Challenge, Model-Based Image Signal Processors via Learnable Dictionaries. Official repo for NTIRE and AIM Challenges

Home Page: https://mv-lab.github.io/model-isp22/

Jupyter Notebook 96.06% Python 3.92% Shell 0.02%
computational-photography computer-vision deep-learning image-processing inverse-problems isp low-level-vision eccv2022 cvpr2022 aim

aisp's Introduction

PhD Researcher in Artificial Intelligence and Computer Vision advised by Prof. Radu Timofte. My current research interests include neural networks, low-level computer vision and photorealism. Kaggle Grandmaster at H2O.ai

Only one who devotes himself to a cause with his whole strength and soul can be a true master. For this reason mastery demands all of a person. Albert Einstein

aisp's People

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hermetico avatar liuxiaoyu1104 avatar mv-lab avatar

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aisp's Issues

Loss information for LPIENET

Hi!

Thank you for your work. Can you give me some details about how gradient loss was used in your paper? Also, what weights (alpha and beta) did you use for SSIM and gradient loss?

About GMACs in LPIENet

Thanks for your excellent work. I use ptflops to calculate the GMACs of LPIENet, but i can't get the same result as in the paper. Could you tell me how do you calculate the GMACs?

LPIE Net!!

Will you release the code of LPIENet?
Thanks~

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