Saturated Reflection Detection for Reflection Removal Based on Convolutional Neural Network

doi:10.1109/ACCESS.2022.3166186

Saturated Reflection Detection for Reflection Removal Based on Convolutional Neural Network

2022-01-01

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DOI：10.1109/ACCESS.2022.3166186

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Single image reflection removal is a technique that removes undesirable reflections, which occur due to glass, from images. Various methods of reflection removal have been proposed, but unfortunately, they usually fail to remove reflections particularly with very high pixel values. In this paper, we define these saturated reflections and their characteristics, as well as discuss and propose a removal system. The proposed system detects areas of saturated reflections based on our proposed model of convolutional neural networks and restores them by a conventional method of image estimation. In our experiments, the proposed system shows better peak-signal-to-noise ratio scores and perceptual quality than conventional methods of reflection removal.

Keywords:

Convolutional neural networks

Glass

Light sources

Estimation

Convolution

Licenses

Laplace equations

Single image reflection removal

reflection detection

convolutional neural network

image inpainting

Saturated Reflection Detection for Reflection Removal Based on Convolutional Neural Network

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