Removal of Gaussian White Noises from the image by probability map prediction based Deep learning approach

Abstract

Deep learning methods have gained an increasing research interest, especially in the field of image denoising. Although there are significant differences between the different types of deep learning techniques used for natural image denoising, it includes significant process and procedure differences between them. To be specific, discriminative learning based on deep learning convolutional neural network (CNN) may effectively solve the problem of Gaussian noise. Deep learning based optimization models are useful in predicting the true noise level. However, no relevant research has attempted to summarize the different deep learning approaches for performing image denoising in one location. It has been suggested to build the proposed framework in parallel with the previously trained CNN to enhance the training speed and accuracy in denoising the Gaussian White Noise (GWN). In the proposed architecture, ground truth maps are created by combining the additional patches of input with original pictures to create ground truth maps. Furthermore, by changing kernel weights for forecasting probability maps, the loss function may be reduced to its smallest value. Besides, it is efficient in terms of processing time with less sparsity while enlarging the objects present in the images. As well as in conventional methods, various performance measures such as PSNR, MSE, and SSIM are computed and compared with one another.