Image Denoising Technology Using Fast and Flexible Denoising Network with Non Local Means

Since years, image denoising plays an important role restoring pristine images from corrupted ones captured digital cameras by ensuring reliable input for high level applications. Traditional approaches for image noising had faced several challenges which include limited noise removal capability, difficulty in handling non-Gaussian noise. Therefore, this research proposes (FFDNet-NLM) for image denoising technology which is employed on SARBM-3D dataset. As this data contains noise, the data is preprocessed by utilizing image normalization which effectively reduces the impact of variations in lighting, contrast and noise. Then, the data is validated by using DnCNN-UNet which efficiently removes Gaussian and non-Gaussian noise by enhancing signal to noise ratio and retained data characteristics. After that, the data is trained by using FFDNet which efficiently removed additive noise and fast interference enabling real time image denoising in NLM. Finally, the combined FFDNet-NLM recovered finer details and textures in image with self-similarity to ensure speed and adaptability varying noise levels and suppressing residual blocks by improved robustness in image denoising. Experimental results of the proposed FFDNet-NLM method attained PSNR (36.05) and SSIM (0.89) are higher than existing method for image denoising techniques such as K-Singular Value Decomposition (K-SVD) and weighted iterative shrinkage thresholding algorithm (WISTA).