Learning a Dilated Residual Network for SAR Image Despeckling

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows superior performance over the state-of-the-art methods on both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table

DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising

Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian white noise). However eliminating real noise is still a very challenging task, since real-world noise often does not simply follow one single type of distribution, and the noise may spatially vary. In this paper, we present a new dual convolutional neural network (CNN) with attention for image blind denoising, named as the DCANet. To the best of our knowledge, the proposed DCANet is the first work that integrates both the dual CNN and attention mechanism for image denoising. The DCANet is composed of a noise estimation network, a spatial and channel attention module (SCAM), and a CNN with a dual structure. The noise estimation network is utilized to estimate the spatial distribution and the noise level in an image. The noisy image and its estimated noise are combined as the input of the SCAM, and a dual CNN contains two different branches is designed to learn the complementary features to obtain the denoised image. The experimental results have verified that the proposed DCANet can suppress both synthetic and real noise effectively. The code of DCANet is available at https://github.com/WenCongWu/DCANet

지문 영상 잡음 제거 및 복원을 위한 심층 합성곱 신경망

학위논문 (석사) -- 서울대학교 대학원 : 자연과학대학 협동과정 계산과학전공, 2021. 2. 강명주.Biometric authentication using fingerprints requires a method for image denoising and inpainting to extract fingerprints from degraded fingerprint images. A few deep learning models for fingerprint image denoising and inpainting were proposed in ChaLearn LAP Inpainting Competition - Track 3, ECCV 2018. In this thesis, a new deep learning model for fingerprint image denoising is proposed. The proposed model is adapted from FusionNet, which is a convolutional neural network based deep learning model for image segmentation. The performance of the proposed model was demonstrated using the dataset from the ECCV 2018 ChaLearn Competition. It was shown that the proposed model obtains better results compared with the models that achieved high performances in the competition.지문을 사용한 생체 인식 인증은 품질이 저하된 지문 영상에서 지문을 추출하기 위한 영상 잡음 제거 및 복원 방법을 필요로 한다. 지문 영상 잡음 제거 및 복원을 위한 몇 가지 딥러닝 모델이 ChaLearn LAP Inpainting Competition - Track 3, ECCV 2018에서 제안되었다. 본 논문에서는 지문 영상 잡음 제거를 위한 새로운 딥러닝 모델을 제안한다. 제안된 모델은 영상 분할을 위한 합성곱 신경망 기반 딥러닝 모델인 FusionNet을 수정하여 작성하였다. 제안된 모델의 성능은 ChaLearn Competition의 데이터셋을 사용하여 검증되었다. 이를 통해 제안된 모델이 대회에서 높은 성능을 획득한 다른 모델들에 비하여 더 나은 결과를 얻음을 확인하였다.Abstract i Contents ii 1 Introduction 1 2 Related Work 3 2.1 Residual Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Convolutional Neural Networks for Semantic Segmentation . . . . . . 4 2.2.1 U-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2.2 FusionNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Recent Trends in Fingerprint Image Denoising . . . . . . . . . . . . . 6 3 Proposed Model 7 3.1 Model Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 Architecture Detail . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2.1 Residual Block . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2.2 Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2.3 Bridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2.4 Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4 Experiments 13 4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.4.1 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.4.2 Comparison with Other Models . . . . . . . . . . . . . . . . 17 5 Conclusion 21 Abstract (In Korean)Maste