An Improved Adaptive Wavelet Thresholding Image Denoising Method

The NeighShrink, IAWDMBNC, and IIDMWT are well-known methods for removal of noise from a corrupted image. But, these methods suffer from optimal recovery of original image for the evident reason that, 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 improved denoising method that provides an adaptive way of setting up minimum threshold by shrinking the wavelet coefficients to overcome the above problem using a modified 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 Noise ratio (PSNR) and Structural Similarity Index Measure (SSIM) better than the neighShrink, IAWDMBNC, and IIDMWT methods.