An Optical physics inspired CNN approach for intrinsic image decomposition

• URL: http://arxiv.org/abs/2105.10076v1
• Date: Fri, 21 May 2021 00:54:01 GMT
• Title: An Optical physics inspired CNN approach for intrinsic image decomposition
• Authors: Harshana Weligampola, Gihan Jayatilaka, Suren Sritharan, Parakrama Ekanayake, Roshan Ragel, Vijitha Herath, Roshan Godaliyadda
• Abstract summary: Intrinsic Image Decomposition is an open problem of generating the constituents of an image. We propose a neural network architecture capable of this decomposition using physics-based parameters derived from the image.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intrinsic Image Decomposition is an open problem of generating the constituents of an image. Generating reflectance and shading from a single image is a challenging task specifically when there is no ground truth. There is a lack of unsupervised learning approaches for decomposing an image into reflectance and shading using a single image. We propose a neural network architecture capable of this decomposition using physics-based parameters derived from the image. Through experimental results, we show that (a) the proposed methodology outperforms the existing deep learning-based IID techniques and (b) the derived parameters improve the efficacy significantly. We conclude with a closer analysis of the results (numerical and example images) showing several avenues for improvement.

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