Image stitching with tone mapping

This is a course project from EECS 332 Intro to Computer Vision, Northwestern University. (2016 winter)

Conventional image stitching method comes as the following four steps:

  1. Scale-Invariant Feature Transform (SIFT)
  2. Feature matching
  3. Random sample consensus (RANSAC)
  4. Center-weighted blending.

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But this is only local blending. It is quite obvious if two images have different white balance. The images above are two pictures I took within a minute and the white balance is changing just as I moved my phone. This color inconsistency happens a lot for outdoor scenes, the camera may arise larger noise and different white balance. We came up with the idea to use the overlapped region for color blending globally.

  • Method: K mean clustering
  • Reason: pixel locations cannot match precisely. Pixel-wise adjustment is not a good choice.¬†Alternatively, we find mutual color regions to match pixel values.

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Overlap region

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k-mean clustering (k = 2)

The k mean clustering is a broadly used method for image segmentation. It can help find the principle clusters.

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Stitching results: up–local stitching; middle–fusing two images; lower–from right to left

The previous case solves the problem of dim environment. What about sunny days?

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When the sun is in the scene, our camera will adjust the exposure time. This will lead to an inconsistency from irradiance to pixel values.

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image stitching results.

We take the idea by high dynamic range (HDR) tone mapping.

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Final tone-mapped result.