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@article{171202,
        author = {Babita Saxena and Dr. R. K . Arya and Dr. Asmita M. Moghe},
        title = {Wavelet based Multilevel Sub-Band Adaptive Thresholding for Image Denoising By PSO},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3086-3091},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171202},
        abstract = {The image de-noising is one of the most widely studied area in the field of image processing. There are many ways (like communication channel, imperfect sensors, interference etc.) by which the noise may affect the image. Depending upon the nature of noise and the image many techniques has been already proposed. However, it is difficult for any technique to operate on wide of noises over different kind of images (like SAR images, X-ray images, Ultrasound images etc.). The best possible solution for such cases is to use adaptive techniques. In this paper we are presenting a multilevel wavelet decomposition based adaptive thresholding technique which utilizes the modified Particle Swarm Optimization (PSO) algorithm to find out the optimal values for thresholds and level of decompositions for given objective function. The modification of PSO is done through random perturbation in particle velocities which induces small randomness in new particle position estimation. This randomness can effectively increase the particle search space, which ultimately provide a much better solution than the conventional PSO. Finally, the proposed algorithm is validated by testing it over different kind of images corrupted by different values of noise.},
        keywords = {Image De-noising, Wavelet Decomposition, Adaptive Thresholding, Particle Swarm Optimization (PSO).},
        month = {December},
        }