In recent years, convolutional neural networks have achieved considerable success in different computer vision tasks, including image denoising. In this work, we present a residual dense neural network (RDUNet) for image denoising based on the densely connected hierarchical network. The encoding and decoding layers of the RDUNet consist of densely connected convolutional layers to reuse the feature maps and local residual learning to avoid the vanishing gradient problem and speed up the learning process. Moreover, global residual learning is adopted such that, instead of directly predicting the denoised image, the model predicts the residual noise of the corrupted image. The algorithm was trained for the case of additive white Gaussian noise and using a wide range of noise levels. Hence, one advantage of the proposal is that the denoising process does not require prior knowledge about the noise level. In order to evaluate the model, we conducted several experiments with natural image databases available online, achieving competitive results compared with state-of-the-art networks for image denoising. For comparison purpose, we use additive Gaussian noise with levels 10, 30, 50. In the case of grayscale images, we achieved

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In recent years, the performance of convolutional neural networks in single-image superresolution has improved significantly. However, most state-of-the-art models address the super-resolution problem for specific scale factors. In this paper, we propose a convolutional neural network for arbitrary scale super-resolution. Specifically, given a range of scale factors, the proposed model can generate superresolution images with non-integer scale factors within the range. The proposed model incorporates a channel-spatial attention block in which the scale factor is also provided. This module recovers the most relevant information from the low-resolution image given the scale factor and enhances the upsampled image before generating the high-resolution target image. This channel attention block allows learning the channel and spatial dependencies. Additionally, we incorporate global residual learning so that the model recovers the details of an upsampled low-resolution image by interpolation. We evaluated the proposed method through extensive experiments on widely used benchmark datasets for single-image super-resolution. In order to assess the performance of the model, we used the peak signal-to-noise ratio and the structural similarity index measure. The proposed model achieves an average of 35.36, 31.78, 29.62 for peak signalto-noise ratio, and 0.9410, 0.8828, 0.8334 for structural similarity index measure for the standard evaluation scale factors ×2, ×3, ×4, respectively. The experimental results show a better performance of the proposed model over other state-of-the-art models for arbitrary scale super-resolution, and are competitive with models trained for specific scale factors.