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Exponential-Ant Cuckoo Search Optimization for image deblurring with spinal cord images based on kernel estimation

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Abstract

In image-related applications, the recorded images are the blurry version of the original image that usually depicts any scene. Due to the optical aberrations, atmospheric distortions, and motion of objects in the scene, blur occurs in the images and degrades image quality. Various image deblurring methods are modeled in the existing works, but accurately recovering the exact true image from the single recorded image or the set of images still results in challenging medical imaging applications. Thus, an effective image deblurring method named Exponential-Ant Cuckoo Search Optimization (Exponential-ACSO) is developed in this research to find the spatial information from the blurred image. The proposed Exponential-ACSO is designed by integrating the Exponential Weighted Moving Average (EWMA) with Ant Lion Optimization (ALO) and Cuckoo Search (CS) algorithm. The computation of new pixel values for noisy pixels makes the image deblurring process more robust and accurate. The objective function is considered to find the optimal fitness value for the parameters of kernel estimation. However, the proposed Exponential-ACSO showed better performance for the metrics, like peak signal-to-noise ratio (PSNR), second derivative like the measure of enhancement (SDME), and structural similarity index (SSIM) with the values 29.756, 33.562, and 0.6988, respectively.

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  1. Department of Electronics and Communication Engineering, Government College of Engineering, Bargur, Krishnagiri, India

    S. Priya & S. Letitia

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Priya, S., Letitia, S. Exponential-Ant Cuckoo Search Optimization for image deblurring with spinal cord images based on kernel estimation. SIViP 16, 339–347 (2022). https://doi.org/10.1007/s11760-021-01929-y

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  • DOI: https://doi.org/10.1007/s11760-021-01929-y

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