In order to overcome blurring due to microscope optics in fluorescence microscopy, we propose a wavelet transform-based non-iterative blind deconvolution method. In our proposed deconvolution algorithm, we used wavelet-based denoising algorithms. We compared discrete wavelet transform (DWT) and wavelet packet transform (WPT) structures as denoising algorithms. WPT-based algorithm resulted in less error than the DWT-based algorithm. Minimum error was obtained for coif5 wavelet type. We compared our denoising methods with several standard denoising methods. Also, we compared our proposed deconvolution algorithm with several standard deconvolution methods. Our proposed wavelet transform-based deconvolution method resulted in the least error compared to other methods. To test the efficacy of our deconvolution method on cell images, we proposed a wavelet entropy-based non-reference image quality (contrast enhancement) metric. We tested our proposed metric by increasing blurring ratio both for noiseless and noisy images. Our metric is useful for evaluating image quality in terms of deblurring.
- Blind deconvolution
- Fluorescence Microscopy
- Non-reference image quality metrics
- Wavelet transform
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering