Experimental Evaluation of Image Denoising Procedure for Real-World Images using Deep Learning

Digital Image denoising is an important research task in picture processing and communication. Traditional statistical methods for image filtering and denoising often struggle with the unpredictable nature of the noise. An updated and high-performing approach employs convolutional neural networks on pictures denoising .This study explores the effectiveness of a denoising Deep-Mode Convolutional Neural Network (Deep-MCNN) model to overcome the challenges faced by developers in the transmission of graphical information during the image data production stage for vision devices. Unlike traditional algorithms, the Deep-MCNN has architectural features that allow it to filter images efficiently even when the noise level is not known. Both quantitative and qualitative studies show that the Peak Signal-to-Noise Ratio (PSNR) of 35.1695 dB, Mean Square Error (MSE) of 294.0662 and Structural Similarity Index (SSIM) of 0.9942 achieved by the proposed model outperforms conventional methods.