Wavelet Image Denoising Based on Improved Thresholding Neural Network and Cycle Spinning

In this paper we propose a new method for image noise reduction based on wavelet transform. In this method we introduce an improved version of thresholding neural networks (TNN) by utilizing a new class of smooth nonlinear thresholding functions as the activation function. Using this approach we will find the best thresholds in the sense of minimum mean square error (MMSE). Then using TNN with obtained thresholds, we employ a cycle-spinning-based technique to reduce image artifacts. Experimental results indicate that the proposed method outperforms several other established wavelet denoising techniques, in terms of peak-signal-to-noise-ratio (PSNR) and visual quality.