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Image denoising always plays a vital role in various engineering bids. Moreover, in image processing technology, image denoising statistics is persisted as a substantial dispute. Over the past decades, certain denoising methods have reached incredible accomplishments. Since there is no much contribution on image denoising considering multimodal and heterogeneous images, this paper motivates us to extend it with the aid of intelligent approach. Dual-tree Complex Wavelet Transform (DT-CWT) is exploited for image transformation for which the wavelet coefficients are estimated using Bayesian Regularization (BR). To ensure the denoising performance for heterogeneous images, the statistical and wavelet features are extracted. Subsequently, the image characteristics are combined with noise spectrum to develop BR model, which estimates the wavelet coefficients for effective denoising. Hence, the proposed denoising algorithm exploits two stages of BR. The first stage predicts the image type, whereas the second stage estimates appropriate wavelet coefficients to DT-CWT for denoising. As a main contribution, the filter coefficients of DT-CWT are optimized by Genetic Algorithm (GA). The performance of the proposed model is analysed in terms of Peak Signal to Noise Ratio (PSNR), Second derivative Measure of Enhancement (SDME), Structural Similarity (SSIM), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson Coefficient (PC), and Symmetric Mean Absolute Percentage Error (SMAPE), respectively. The proposed model is compared to the conventional models, and the significance of the developed model is clearly described. From the analysis, it is observed that the PSNR of the developed model is 69.97%, 5.85%, 76.91%, 33.38%, 46.40%, and 46.44% better than 2D SMCWT, DT-CWT, DT-CDWT, DT-RDWT, W-ST, and W-HT, respectively. Similarly, for SSIM measure, the proposed model has great deviation over conventional methods, and the model is 19.17%, 83.66%, 24.65%, 72.99%, and 73.15% better than DT-CWT, DT-CDWT, DT-RDWT, W-ST, and W-HT, respectively.

Dual Stage Bayesian Network with Dual-Tree Complex Wavelet Transformation for Image Denoising