###### Abstract  
Deep learning technology dominates current research in image denoising. However, denoising performance is limited by target noise feature loss from information propagation in association with the depth of the network. This paper proposes a Dense Residual Feature Extraction Network (DRFENet) combined with a Dense Enhancement Block (DEB), a Residual Dilated Block (RDB), a Feature Enhancement Block (FEB), and a Simultaneous Iterative Reconstruction Block (SIRB). The DEB uses our proposed interval transmission strategy to enhance the extraction of noise features in the initial stage of the network. The RDB module uses a combination strategy of concatenated dilated convolution and a skip connection, and the local features are amplified through different perceptual dimensions. The FEB enhances local feature information. The SIRB uses an attention block to learn the noise distribution while using residual learning (RL) technology to reconstruct a denoised image. The combination strategy in DRFENet makes the neural network deeper to obtain higher fine-grained image information. We respectively examined the performance of DRFENet in gray image denoising on datasets BSD68 and SET12 and color image denoising on datasets McMaster, Kodak24, and CBSD68. The experimental results showed that the denoising accuracy of DRFENet is better than most existing image-denoising methods under PSNR and SSIM evaluation indicators. Keywords:image denoising; deep-learning technology; dilated skip convolution; residual neural network; deep neural networks; computer vision; feature extraction and analysis +
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a map of noise, then subtract that noise map from the noisy image to obtain a clean image. Thus, these methods focus on noise learning and image restoring. [PERSON] et al. proposed the DnCNN model [10], which employs the residual learning (RL) method based on CNN to general noise. Unlike traditional denoising algorithms, DnCNN can capture the high-level feature of uniform Gaussian noise within a certain scale. FFDNet optimizes DnCNN by introducing a tunable noise level map as an augmented input for CNN architecture [11]. FFDNet exhibits competitive performance in denoising real images, including noise on different levels and spatially variant noise. The Convolutional Blind Denoising Network (CBDNet) improves FFDNet by combining two CNNs: a noise estimation subnetwork and a non-blind denoising subnetwork [12]. To improve the model's noise generalization, both synthetic and real images are used to train the model. Further, both the Poisson-Gaussian model and in-camera processing pipelines are used to estimate noise. A related model, RIDNet [13], uses dilation convolution to increase the receptor field and applies an attention mechanism to exploit the channel dependencies. This strategy achieves excellent results, with an improvement of 0.11 dB over DnCNN on the BSD68 dataset (\(\sigma=25\)). Although the methods above obtain competitive performance in image denoising, the following limitations remain:  
1. Since the deep learning network is a nonlinear transformation module, the deeper model means more complex nonlinearity. As the depth increases, the parameters will also increase. When the feature information is introduced from a shallow layer to deep layer, it will undergo nonlinear transformation many times, which may cause some features to lose their own meaning. Therefore, the network depth limits the flow of information, leading the communication ability between the deep layers and the shallow layers of the model to be low. In the performance, it is more likely to cause the phenomenon of overfitting, resulting in insufficient generalization ability. 2. The pooling layers are essential in expanding the receptive field. However, pixel information is lost in the process of pooling, which diminishes the influence of shallow layers on deep layers. 3. In an image background whose color is very similar to that of Gaussian noise, the noise blended into the background is difficult to distinguish.