Removal of specular reflections from image sequences using feature correspondences

Abstract

The presence of specular highlights can hide underlying features of a scene within an image and can be problematic in many application scenarios. In particular, this poses a significant challenge for applications where image stitching is used to create a single static image of a scene from inspection footage of pipes, gas tubes, train tracks and concrete structures. Furthermore, they can hide small defects in the images causing them to be missed during inspection. We present a method which exploits additional information in neighbouring frames from video footage to reduce specularity from each frame. The technique first automatically determines frames which contain overlapping regions before the relationship that exists between them is exploited in order to suppress the effects of specular reflections. This results in an image that is free from specular highlights provided there is at least one frame present in the sequence where a given pixel is present in a diffuse form. The method is shown to work well on greyscale as well as colour images and effectively reduces specularity and significantly improves the quality of the stitched image, even in the presence of noise. While applied to the challenge of reducing specularity in inspection videos, the method improves upon the state-of-the-art in specularity removal, and its applications are wide-ranging as a general purpose pre-processing tool.

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Acknowledgements

The authors would like to thank Rahul Summan and Francesco Guarato for useful discussions about NDE applications and for their guidance for data acquisition.

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Correspondence to Syed. M. Z. Abbas Shah.

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Shah, S.M.Z.A., Marshall, S. & Murray, P. Removal of specular reflections from image sequences using feature correspondences. Machine Vision and Applications 28, 409–420 (2017). https://doi.org/10.1007/s00138-017-0826-6

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Keywords

  • Image Projection
  • Specular Reflection Removal
  • Visual Inspection
  • Non-Destructive Evaluation