A deep learning algorithm for Gaussian noise removal from both grayscale and color images is developed. As opposed to most existing discriminative methods that train a specific model for each noise level, the proposed method can handle a wide range of noise levels using only two trained models, one for low noise levels and the other for high noise levels. In the proposed algorithm, the training process consists of three successive steps. In the first step, a classifier is trained to classify the noisy and clean images. In the second step, a denoiser network aims to remove the noise in the image features that are extracted by the trained classifier. Finally, a decoder is utilized to map back the denoised images features into images pixels. To evaluate the performance of the model, the Berkeley segmentation dataset of 68 images (BSDS68) and 12 widely used images are used, and the denoising performance for additive white Gaussian noise is compared with several state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR) and visual quality. For grayscale image denoising of BSDS68, our method gives the highest PSNR on all noise levels (significant mean improvement of 0.99). For color image denoising of BSDS68, except for one low noise level, the proposed method gives the highest PSNR on all other noise levels (mean improvement of 0.3).
The search for effective noise removal algorithms is still a real challenge in the field of image processing. An efficient image denoising method is proposed for images that are corrupted by salt-and-pepper noise. Salt-and-pepper noise takes either the minimum or maximum intensity, so the proposed method restores the image by processing the pixels whose values are either 0 or 255 (assuming an 8-bit/pixel image). For low levels of noise corruption (less than or equal to 50% noise density), the method employs the modified mean filter (MMF), while for heavy noise corruption, noisy pixels values are replaced by the weighted average of the MMF and the total variation of corrupted pixels, which is minimized using convex optimization. Two fuzzy systems are used to determine the weights for taking average. To evaluate the performance of the algorithm, several test images with different noise levels are restored, and the results are quantitatively measured by peak signal-to-noise ratio and mean absolute error. The results show that the proposed scheme gives considerable noise suppression up to a noise density of 90%, while almost completely maintaining edges and fine details of the original image.