Mathematical models for noise reduction in IoT image processing applications

IoT has opened the floodgates the importance use of image processing in areas like surveillance, auto-navigating vehicles, and smart healthcare. Nonetheless, noise in IoT-generated images largely distorts image quality as well as subsequent analytical operations that may be performed. The fundamental goal is to derive and test stable mathematical models that improve noise reduction while maintaining real-time computation that would be viable for limited IoT devices. The paper uses both first order Linear filtering techniques like Gaussian filters and Median and second order filters which are in the transform domain like Wavelet transforms. These techniques are then systematically invoked on images contaminated by Gaussian, Salt-and-Pepper and Speckle noise types for performance evaluation. Simulation studies using MATLAB with camera man image ‘cameraman.tif’ for standard prove the Wavelet transforms outperforms in noise reduction attaining up to 15.3 dB PSNR and 0.92 SSIM for better structural preservation. On the other hand, Gaussian and Median filters take 0.05 – 0.07 seconds to process and hence well suited for real-time applications.