Abstract
It is the age of social media and influencer culture and the importance of style and aesthetic has never been more apparent. Photo-retouching remains a critical and often, time consuming part of this process. To simplify the complex and skillful task of retouching, this paper proposes a Deep Learning based automatic photo retouching solution. It builds upon a GAN-based architecture, trained in an unsupervised manner to instantly enhance images. However, most Deep Learning based approaches are black-boxes and provide the user little to no insight into the enhancements applied, leaving it difficult to perform minor tweaking in enhancement as per preference. Hence, we also propose an algorithm to estimate parametric modifications given a pair of images with the aim of enabling the user more in the process of image enhancement. Together these allow complete transparency in deep learning based image enhancement while still producing results comparable to black-box approaches. We demonstrate the effectiveness of the proposed solution by comparing Mean Opinion Scores (MoS) and show that more than 84% of the users prefer the proposed solution.
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Sharma, M., Mittar, R., Chakraborty, P. (2021). Deep Learning Based Image Enhancement and Black Box Filter Parameter Estimation. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_15
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DOI: https://doi.org/10.1007/978-981-16-1092-9_15
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