Intelligent & Adaptive Image Denoising Based on Wavelets with Shrinkage Rule

Real time image processing should have high quality results at lesser computational time. A spatial image denoising method using 2D discrete wavelet transform (2D DWT) and Stationary wavelet transform (SWT) is presented in this paper for high quality image results. DWT gives high quality output at low levels of noise at relatively lesser time consumption where as it suffers from translation invariance. SWT overcomes translational invariance by removing the up samplers and down samplers and gives better image quality but consumes more computational time. Considering the advantages of both SWT and DWT, an attempt is made to propose an adaptive and intelligent denoising system which is based on both these wavelet transform methods. The operational schema involves three distinct approaches. The first approach is a buffer unit for approximately clean image. The second approach is based on SWT, and the effects of noise are minimized by soft thresholding at high frequency sub bands. The third approach is based on both SWT and DWT, the noise present in the high frequency sub bands of SWT and DWT are minimized by soft thresholding, and then modified high frequency sub bands of DWT are interpolated with modified high frequency sub bands of SWT using bicubic interpolation technique. The result is a noise free higher order sub bands which are combined with average band of SWT. Finally the resultant bands undergo inverse SWT for denoised output. The experimentation was performed on two different medical images; Peak signal to noise ratio (PSNR) is obtained for various noise variances ranging from 4 to 40 dB. The results shows that the proposed system is more suitable for real time applications where the time constraints are critical while giving high 90 R. Vijaya Arjunan and V. Vijaya Kumar quality image output especially at low to moderate level of noise. In this work an intelligent denoising system is introduced that makes trade-off between the quality of denoised image and the time required for image denoising.