IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2020

Multi-level Edge Features Guided Network for Image Denoising

Faming Fang1     Juncheng Li1     Yiting Yuan1     Tieyong Zeng2     Guixu Zhang1    

1 East China Normal University     2 The Chinese University of Hong Kong    

  Author names alphabetically      Contact us: cvjunchengli@gmail.con

  This achievement was completed during the visit of the Chinese University of Hong Kong



Abstract


Image denoising is a challenging inverse problem due to the complex scenes and information loss. Recently, various methods have been considered to solve this problem by building a well-designed convolutional neural network (CNN) or introducing some hand-designed image priors. Different from previous works, we investigate a new framework for image denoising, which integrates edge detection, edge guidance, and image denoising into an end-to-end CNN model. To achieve this goal, we propose a Multi-level Edge Features Guided Network (MLEFGN). Firstly, we build an edge reconstruction network (Edge-Net) to directly predict clear edges from the noisy image. Then, the Edge-Net is embedded as part of the model to provide edge priors and a dual-path network is applied to extract image and edge features, respectively. Finally, we introduce a multilevel edge features guidance mechanism for image denoising. To the best of our knowledge, the Edge-Net is the first CNN model specially designed to reconstruct image edges from the noisy image, which shows good accuracy and robustness on natural images. Extensive experiments clearly illustrate that our MLEFGN achieves favorable performance against other methods and plenty of ablation studies demonstrate the effectiveness of our proposed Edge-Net and MLEFGN.


Motivation


The most widely used method is to apply off-the-shelf edge detectors on the degraded image to obtain image edges. However, (1). Existing edge extractors are extremely sensitive to noise or other interference factors; (2). It is extremely difficult to obtain clear and accurate edges from the degraded image using off-the-shelf edge operators; (3). Inaccurate edges will interfere with the quality of the reconstructed images. Therefore, we aim to explore a CNN model that can reconstruct clear and accurate soft-edges from the noisy image directly.


MLEFGN



Visual Results



Reconstructed Edges



PSNR/SSIM Results



Downloads


Paper : [ TNNLS2020_MLEFGN.pdf ]
Experimental results : [ MLEFGN_SR.zip ]
Source Code. : [ Code ]


BibTex


@InProceedings{fang2020multi,
    title = {Multi-level Edge Features Guided Network for Image Denoising},
    author = {Fang, Faming and Li, Juncheng, Yuan Yiting, Zeng, Tieyong, and Zhang Guixu},
    booktitle = {IEEE Transactions on Neural Networks and Learning Systems},
    year = {2020},
    publisher = {IEEE}
}