An efficient image-denoising method using a deep learning technique

Numerous difficulties arise while denoising images in image processing at present. The article indicates a unique deep convolution neural network-based image denoising (DCNN). This article presents a DCNN to generate the noisy image as opposed to earlier learning-based methodologies. Therefore, it is possible to extract the clean latent image from the deformed image by removing the noise. Compared to other scenarios, this strategy is the most effective. Although some of the already existing systems included machine learning techniques in addition to filters such as wiener, median, and mean filters, they were unable to produce reliable results. Experimental results show that the suggested de-noising methodology outperforms the current denoising techniques. We may say that this strategy will aid in obtaining better results by looking at factors such as PSNR, MSE, training loss, and training accuracy. The conclusions also demonstrate that multiple noises with various noise levels can be suppressed using a single denoising model.