DESIGN OF HYBRID ADAPTIVE MODEL FOR IMAGE DENOISING

Abstract

This thesis work proposes a new denoising algorithm based on Particle filter and Wavelet (Curvelet) transform combination, particle filter generates weights through SIR algorithm to cancel the interference of noise present in the image, while curvelet transform is used to shrink the remaining segments of noise, so this method can both remove image blurr and maintain good texture as well. The PF+Clet Image Denoiser is successfully designed and implemented, which is a new approach in image enhancement and Interference cancellation. This thesis concludes that it is quite efficient algorithm among other adaptive filtering techniques. Experimental results also show that proposed algorithm performs extremely well when noise density is increased, as obtained image is completely visible. This approach comprises of generation of particles by performing weight normalization, resampling and update state. Therefore, for large number of particles execution time is more when compared to other adaptive filtering approach. Key Words: Particle Filter(PF), Curvelet(CLet), Wavelet(Wlet), Peak signal to noise Ratio(PSNR), Deblurring.