OPERA net Otsu driven performance enhanced image restoration algorithm

Digital images have progressed significantly in many areas but various types of noise still exist in real-world images such as Gaussian noise, Poisson noise, Salt-and-pepper noise so on. Although image denoising plays an important role to remove noise from images by using many techniques to preserve the important feature of an image but faces the problem of computational complexity, over smoothing. To address these limitations proposed paper introduces hybrid image denoising technique that combines the strengths of Wavelet transform and Non-Local Means (NLM) filtering, with enhanced Otsu thresholding. In proposed work, firstly the noisy image is denoised by wavelet-based Otsu thresholding and then NLM is employed to improve the enhance edge preservation of an image by eliminating the further noise. Kodak 24 dataset is used to test the images. Furthermore, we compare the proposed technique to the existing technique based on the performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Root Mean Squared Error (RMSE). The Kodak24 dataset is used to evaluate the proposed hybrid denoising method. Experimental results show that the proposed technique outperforms existing approaches in terms of PSNR (34.86), SSIM (0.93) and RMSE (4.61) demonstrating its effectiveness in denoising and preserving structural image quality. Additionally, the FOM and VIF scores indicate the outstanding results of the hybrid strategy: both the FOM and VIF values were the best among all other assessed denoising methods, attained to 0.99 and 0.59, respectively.