A Novel Approach for Image Denoising and Performance Analysis using SGO and APSO
V Mnssvkr Gupta, KVSS Murthy, R Shiva Shankar
Abstract
Abstract Image denoising is essential to extract the information contained in an image without errors. A technique of using both wavelets and evolutionary computing tools is proposed to denoise and to improve the image quality. An adaptive thresholding-based wavelet denoising technique in the threshold function is coordinated by novel social group optimization (SGO) and accelerated particle swarm optimization (APSO) is proposed. The simulation oriented experimentation is taken out employing MATLAB and the analysis is carried out using the image property metrics similar to peak signal to noise ratio (PSNR), mean square error (MSE) and other structural similarity index metrics (SSIM).
Topics & Concepts
Particle swarm optimizationThresholdingPeak signal-to-noise ratioNoise reductionWaveletArtificial intelligenceMean squared errorImage (mathematics)MATLABComputer sciencePattern recognition (psychology)Noise (video)Non-local meansImage qualityMathematicsImage processingAlgorithmStatisticsOperating systemImage and Signal Denoising MethodsAdvanced Image Fusion TechniquesOptical Coherence Tomography Applications