Combination of Spatial Filtering and Adaptive Wavelet Thresholding for Image Denoising

Abstract

Abstract-Thresholding in wavelet domain has proven very high performances in image denoising and particularly for homogeneous ones. Conversely, and in cases of relatively non-homogeneous scenes, it often induces the loss of some true coefficients; inducing so, to smoothing the details and the different features of the thresholded image. Therefore, and in order to overcome this shortcoming, we introduce within this paper a new alternative made by a combination of advantages of both spatial filtering and wavelet thresholding; that ensures well removing the noise effect while preserving the different features of the considered image. First, the degraded image is decomposed into wavelet coefficients via a 2-level 2D-DWT. Then, the finest detail sub-bands likely due to noise, are thresholded in order to maximally cancel the noise contribution. The remaining noise shared across the coarse detail subbands (LH2, HL2, and HH2) is cleaned by filtering these mentioned sub-bands via an adaptive wiener filter instead of thresholding them; avoiding so smoothing the acquired image. Finally, a joint bilateral filter (JBF) is applied to ensure the preservation of the different image features. Experimental results show notable performances of our new proposed scheme compared to the recent state-of-the-art schemes visually and in terms of (MSE), (PSNR) and correlation coefficient.

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

“…When the noise density is high, the distance between the regular entries (in the windows that have high-size) and the Centre pixel seems to be large. As such, a drawback is observed in ARMF when the noise densities exceed 80 percent [38].…”

“…The main differentiation of this technique among the others, like spatial domain filtering technique, that transforms a domain of filtering technique for the given noisy image into another domain, and then it applies the noise reduction procedure on the newly constructed image by following various characteristics of the image and its noise as a second step (Higher coefficients indicates the high-frequency part, i.e., image edges or details, lower coefficients indicate the noise) [38,40]. Researchers have done many experiments to enhance denoising efficiency by mixing the wavelet transform filtering domain with the spatial domain filtering methods.…”

Wavelet-Based Self-adaptive Hierarchical Thresholding Algorithm and Its Application in Image Denoising

“…Other commonly used wavelet denoising algorithms also have obvious limitations. For instance, the wavelet transform modulus maxima (WTMM) method cannot achieve satisfactory denoising effect, because its calculation accuracy is affected by various factors [7,8]. In correlation-based wavelet denoising, the noises can be suppressed effectively through multi-layer decomposition, but few details are available through the decomposition.…”