Superresolution of Hyperspectral Image Using Advanced Nonlocal Means Filter and Iterative Back Projection

Abstract EN

We introduce an efficient superresolution algorithm based on advanced nonlocal means (NLM) filter and iterative back projection for hyperspectral image.

The nonlocal means method achieves the to-be-interpolated pixel by the weighted average of all pixels within an image, and the unrelated neighborhoods are automatically eliminated by the trivial weights.

However, spatial location distance is also an important issue to reconstruct the missing pixel.

Therefore, we proposed an advanced NLM (ANLM) filter considering both neighborhood similarity and patch distance.

In the conventional NLM method, the search region was the whole image, while the proposed ANLM utilizes the limited search to reduce the complexity.

The iterative back projection (IBP) is a very famous method to deal with the image restoration.

In the superresolution issue, IBP is able to recover the high-resolution image iteratively from the given low-resolution image which is blurred due to the noise by minimizing the reconstruction error, while, because the reconstruction error of IBP is back projection and isotropic, the conventional IBP suffers from jaggy and ringing artifacts.

Introducing the ANLM method to improve the visual quality is necessary.