A Hybrid Approach for Smoothening and Denoising of an Image Using Enhanced Oversampling Algorithm

In imaging science, image processing is any form of signal processing for which the image or video frame is an input, the output of image processing may be either an image or a set of characteristics or parameters related to the image. Phase retrieval of oversampled diffraction patterns is fundamentally limited by investigational noise. It remains a challenge to perform steady phase retrieval of weakly scattering objects such as biological specimens from noisy experimental data. When a coherent wave illuminates a noncrystalline specimen, the diffraction intensities in the far field are continuous and can be sampled at a frequency finer than the Nyquist interval (i. e. oversampled). Existing methodology works on iterative approach for phase retrieval of linearly distributed noisy image also the system does not have any image enhancement after reconstruction. This system works for phase retrieval of linearly/non linearly distributed noisy diffracted images, also this system provides image enhancement of the reconstructed image by using three different filters i. e. Inverse filter, Wiener filter and Lucy Richardson filter and the best reconstruction is compared by MSE and PSNR values of the resulting image. After simulating so many images for both linearly distributed noisy image and non linearly distributed noisy image we conclude that Inverse filter is giving better reconstructions.