An adaptive wavelet thresholding image denoising method

The NeighShrink, IAWDMBNC, and IIDMWT are important methods to remove the noise from a corrupted image. These methods however cannot recover original image significantly since the threshold value does not minimize the noisy wavelet coefficients across scales and thus they do not give good quality of image. In this paper, we propose an adaptive denoising method that provides an adaptive way of setting up minimum threshold by shrinking the wavelet coefficients to overcome the above problem using an exponential function. Our method retains the original image information efficiently by removing noise and it has the image quality parameters such as peak-to-signal nose ratio (PSNR) and Structural Similarity Index Measure (SSIM) better than the NeighShrink, IAWDMBNC, and IIDMWT methods.