Performance Comparison of Improved Wavelet Based Color Image Denoising Using Shrinkage Methods

Removing noise from the original signal is still a challenging problem for researchers. There have been several algorithms and each approach has its assumptions, advantages, and limitations. This paper proposes an effective image denoising of color images in multiresolution transform domain using modified adaptive shrinkage. Most traditional noise reduction method tends to over-suppress high-frequency details. For overcoming this problem the input image is first decomposed into flat and edge regions. Noise is removed using the alpha map computed from wavelet transform coefficients of LH, HL, and HH bands. Noise is removed in the flat regions by Inner Product method. After removing noise in the flat regions, further noise removal is done in the edge regions using different types of wavelet shrinkage functions. Experimental results show that the NeighShrink can effectively reduce noise without losing sharp details in the noisy images and is suitable for commercial low-cost imaging systems.