Thesis

Image fusion superresolution in structured illumination microscopy

The limited resolution of a microscope is due to the diffraction limit, aperture and the optical lens. Superresolution (SR) methods improve resolution beyond the diffraction limit. Structured illumination (SI) is an SR method that helps acquire and fuse several non-redundant low-resolution (LR) images of the same object to produce a high-resolution (HR) image. In this thesis, an alternative method is developed and evaluated for fusing LR images obtained using SI to produce HR images. The method advocates the use of the L1 norm with total variation regularization to address the problem with existing image reconstruction using Wiener-like deconvolution. The method is applicable for reconstruction of grayscale images. The work also justifies some practical assumptions that greatly reduce the computational complexity and memory requirements of the proposed methods. The work introduces Peak Signal to Standard Error of the Estimate Ratio (PSSEER) as a quantitative method of measuring image quality. Subjective and objective methods are consistent in showing that L1/TV optimization resolves more details than Wiener-like deconvolution reconstruction. The proposed method performs better in the absence of noise and in the presence of either Gaussian or Poisson noise.

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