An Efficient Spectral Domain Approach of Periodic Noise Suppression in Digital Images using Gaussian Filtering Profile

Research area based on periodic noise fading from digital images stands distinctively and uniquely in the recent research field of image processing due to its enormous appellation amongst the researchers over the last few decades. Images are often contaminated by periodic noises (unintended and spatially dependent repetitive pattern) which considerably demean image quality. Separation of high amplitude noisy spectra by using appropriate thresholding method becomes easier as periodic noisy spectral components concentrate in image spectrum and becomes clearly noticeable from the remaining uncorrupted spectral components. Here, we have proposed a simple yet graceful fully adaptive periodic noise reduction algorithm. Atlast double thresholding method has been exploited to get the noisy bitmap using the concept of spectral histogram after spectral smoothing operation. After that, approximate shapes of each noisy region are detected in an elegant way. Lastly, to filter out those noisy components, an Adaptive Gaussian Restoration Filter (AGRF) with no prefixed coefficients has been applied in the filtration stage. Performance of our proposed algorithm has been judged with other existing algorithms as in the literature which demarcates that our proposition is able to achieve more effective restoration with considerable lower computational time.