Objective

2

Image denoising is a classical image reconstruction problem in low-level computer vision.It estimates the latent clean image from a noisy one.Digital images are often affected by the noise caused by imaging equipment and external environment in the process of digitization and transmission.Although several methods have achieved reasonable results in recent years

they rarely mentioned the over-smoothing effects and the loss of edge details.Thus

a novel image denoising method via residual learning based on edge enhancement is proposed.

Method

2

Recently

due to its powerful learning ability

very deep convolutional neural network has been widely used for image restoration.Inspired by ResNet

unlike other direct denoising networks

identity mappings are introduced to enable our residual network to increase the depth

and then slightly modify the architecture to adapt better to the denoising task.Pooling layers and batch normalization are removed to preserve details.Instead of these

high-frequency layer decomposition and global skip connection are used to prevent over-fitting.They change the input and output of the network to reduce the solution space.To speed up the training process

we select the rectified linear unit (ReLU) as the activation function and remove it before the convolution layer.Traditionally

image restoration work used the per-pixel loss between the ground truth and the restored image as the optimization target to obtain excellent quantitative scores.However

in recent research

minimizing pixel-wise errors only on the basis of low-level pixels has proven prone to loss of details and smoothens the results.Meanwhile

the perceptual loss function has shown that it can generate high-quality images with a better visual performance by capturing the difference between the high-level feature representations

but it sometimes fails to preserve color and local spatial information.To combine both benefits

we propose a new joint loss function that consists of a normal pixel-to-pixel loss and a perceptual loss with appropriate weights.In summary

the flow of our method is described as follows.First

the high-frequency layer of the noisy image is used as the input by removing the background information.Then

a residual mapping is trained to predict the difference between clean and noisy images as output instead of the final denoised image.The denoised result is improved further

and a joint loss function is defined as the weighted sum of the pixel-to-pixel Euclidean and perceptual losses.A well-trained convolutional neural network is connected to learn the semantic information

which we will likely measure in our perceptual loss.This setup encourages the train process to learn similar feature representations rather than match each low-level pixel

which can guide the front denoising network in reconstructing more edges and details.Unlike normal denoising models for only one specific noise level

our single model can deal with the noise of unknown levels (i.e.

blind denoising).We employ CBSD400 as the training set and evaluate the quality in Set5

Set14

and CBSD100 with noise levels of 15

25

and 50

respectively.To train the network for a specific noise level

we generate the noisy images by adding Gaussian noise with standard deviations of

σ

=15

25

50.Alternatively

we train a single blind network for the unknown noise range [1

50].

Result

2

To verify the effectiveness of the proposed network

we show the quantitative and qualitative results of our method in comparison to those of state-of-the-art methods

including BM3D

TNRD

and DnCNN.The performance of the algorithm is evaluated by the peak signal-to-noise ratio as the quantitative indicator.Results show that the proposed network training with MSE loss can solely produce the best index results.The proposed algorithm (MSE-S) is better by 0.63 dB、0.55 dB and 0.17 dB compared with BM3D

TNRD

and DnCNN

respectively.In the qualitative visual sense

the perceptual loss model proposed in this paper clearly achieves a clearer denoising result.Compared with the fuzzy regions generated by other methods

this method preserves more edge information and texture details.We perform another experiment to show the ability of blind denoising.The input is composed of noisy parts with three levels

10

30

and 50.Results indicate that our blind model can generate a satisfactory restored output without artifacts even when the input is corrupted by several levels of noise in different parts.

Conclusion

2

In this paper

we describe a deep residual denoising network of 26 weight layers where perceptual loss is adopted to enhance the information detail.Residual learning and high-frequency layer decomposition are used to reduce the solution space to speed up the training process without pooling layers and batch normalization.Unlike the normal denoising model for only one specific noise level

our new model can deal with blind denoising problems with different unknown noise levels.The experiments show that the proposed network achieves superior performances both in quantitative and qualitative results

and recovers majority of the missing details from low-quality observations.In the future

we will explore how to handle other kinds of noise

especially the complex real-world noise

and consider a single comprehensive network for more image restoration tasks.In addition

we will likely focus on researching more visually perceptible indicators in addition to PSNR.