• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4439-4455
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• 2021

Compared with two-stage object detection algorithms, one-stage algorithms provide a better trade-off between real-time performance and accuracy. However, these methods treat the intermediate features equally, which lacks the flexibility to emphasize meaningful information for classification and location. Besides, they ignore the interaction of contextual information from different scales, which is important for medium and small objects detection. To tackle these problems, we propose an image pyramid network based on dual attention mechanism (DAIPNet), which builds an image pyramid to enrich the spatial information while emphasizing multi-scale informative features based on dual attention mechanisms for one-stage object detection. Our framework utilizes a pre-trained backbone as standard detection network, where the designed image pyramid network (IPN) is used as auxiliary network to provide complementary information. Here, the dual attention mechanism is composed of the adaptive feature fusion module (AFFM) and the progressive attention fusion module (PAFM). AFFM is designed to automatically pay attention to the feature maps with different importance from the backbone and auxiliary network, while PAFM is utilized to adaptively learn the channel attentive information in the context transfer process. Furthermore, in the IPN, we build an image pyramid to extract scale-wise features from downsampled images of different scales, where the features are further fused at different states to enrich scale-wise information and learn more comprehensive feature representations. Experimental results are shown on MS COCO dataset. Our proposed detector with a 300 × 300 input achieves superior performance of 32.6% mAP on the MS COCO test-dev compared with state-of-the-art methods.

• 방송공학회논문지
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• 제27권3호
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• pp.283-294
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• 2022

딥 러닝 네트워크에서 사용되는 특징 맵은 일반적으로 영상보다 데이터가 크며 특징 맵을 전송하기 위해서는 영상의 압축률보다 더 높은 압축률이 요구된다. 본 논문은 딥러닝 기반의 영상처리에서 객체의 크기에 대한 강인성을 가지는 FPN 구조의 네트워크에서 사용되는 피라미드 특징 맵을 높은 압축률로 전송하기 위해 제안한 복원-예측 네트워크를 통해 전송된 일부 계층의 피라미드 특징 맵으로 전송하지 않은 계층의 피라미드 특징 맵을 예측하며, 압축으로 인한 손상을 복원하는 구조를 제안한다. 제안한 방법의 COCO 데이터셋 2017 Train images에 대한 객체 탐지의 성능은 rate-precision 그래프에서 VTM12.0을 통해 특징 맵을 압축한 결과 대비 BD-rate 31.25%의 성능향상을 보였고, PCA와 DeepCABAC을 통한 압축을 수행한 방법 대비 BD-rate 57.79%의 성능향상을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1761-1777
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• 2021

An image with infrared features and visible details is obtained by processing infrared and visible images. In this paper, a fusion method based on Laplacian pyramid and generative adversarial network is proposed to obtain high quality fusion images, termed as Laplacian-GAN. Firstly, the base and detail layers are obtained by decomposing the source images. Secondly, we utilize the Laplacian pyramid-based method to fuse these base layers to obtain more information of the base layer. Thirdly, the detail part is fused by a generative adversarial network. In addition, generative adversarial network avoids the manual design complicated fusion rules. Finally, the fused base layer and fused detail layer are reconstructed to obtain the fused image. Experimental results demonstrate that the proposed method can obtain state-of-the-art fusion performance in both visual quality and objective assessment. In terms of visual observation, the fusion image obtained by Laplacian-GAN algorithm in this paper is clearer in detail. At the same time, in the six metrics of MI, AG, EI, MS_SSIM, Q_abf and SCD, the algorithm presented in this paper has improved by 0.62%, 7.10%, 14.53%, 12.18%, 34.33% and 12.23%, respectively, compared with the best of the other three algorithms.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.149-154
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• 2023

This paper presents a novel approach to multi-focus image fusion using light field cameras. The proposed neural network, LFFCNN (Light Field Focus Convolutional Neural Network), is composed of three main modules: feature extraction, feature fusion, and feature reconstruction. Specifically, the feature extraction module incorporates SPP (Spatial Pyramid Pooling) to effectively handle images of various scales. Experimental results demonstrate that the proposed model not only effectively fuses a single All-in-Focus image from images with multi focus images but also offers more efficient and robust focus fusion compared to existing methods.

• 융합신호처리학회논문지
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• 제15권2호
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• pp.24-29
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• 2014

X-선 유방촬영술은 유방암의 조기발견을 위해 가장 일반적으로 이용되고 있다. 유방암의 조기 발견과 진단의 효율성을 증가시키기 위하여 많은 영상향상 방법들이 연구개발 되었다. 본 논문은 디지털 맘모그램을 위하여 라플라시안 피라미드에서 대비척도를 이용한 다중 스케일 대비 향상 방법을 제안한다. 제안한 방법은 입력 영상을 가우시안 피라미드와 라플라시안 피라미드로 분해하고, 분해된 다해상도 영상의 피라미드 계수들은 저주파수 성분들과 고주파수 성분들의 비율로 대역 제한된 국부 대비척도를 정의한다. 대비 향상을 위하여 정의된 대비척도를 이용하여 분해된 피라미드 계수들을 수정하고, 수정된 계수들로 피라미드 복원 과정을 거처 최종 향상된 영상을 얻는다. 제안된 방법의 성능은 실험을 통하여 기존 방법들과 향상결과를 비교하고, 대비 측정 알고리즘을 이용한 정량적인 평가결과에서 우수한 성능을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1778-1797
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• 2021

Document image binarization is an important pre-processing step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of encoder-decoder architectures, such as FCN (fully convolutional network) and U-Net. Despite their success, they still suffer from three limitations: (1) reduced feature map resolution due to consecutive strided pooling or convolutions, (2) multiple scales of target objects, and (3) reduced localization accuracy due to the built-in invariance of deep convolutional neural networks (DCNNs). To overcome these three challenges, we propose an improved semantic segmentation model, referred to as DP-LinkNet, which adopts the D-LinkNet architecture as its backbone, with the proposed hybrid dilated convolution (HDC) and spatial pyramid pooling (SPP) modules between the encoder and the decoder. Extensive experiments are conducted on recent document image binarization competition (DIBCO) and handwritten document image binarization competition (H-DIBCO) benchmark datasets. Results show that our proposed DP-LinkNet outperforms other state-of-the-art techniques by a large margin. Our implementation and the pre-trained models are available at https://github.com/beargolden/DP-LinkNet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• Journal of applied mathematics & informatics
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• 제42권4호
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• pp.931-943
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• 2024

Accurately identifying brain tumors is crucial for medical imaging's precise diagnosis and treatment planning. This study presents a novel approach that uses cutting-edge image processing techniques to automatically segment brain tumors. with the use of the Pyramid Network algorithm. This technique accurately and robustly delineates tumor borders in MRI images. Our strategy incorporates special algorithms that efficiently address problems such as tumor heterogeneity and size and shape fluctuations. An assessment using the RESECT Dataset confirms the validity and reliability of the method and yields promising results in terms of accuracy and computing efficiency. This method has a great deal of promise to help physicians accurately identify tumors and assess the efficacy of treatments, which could lead to higher standards of care in the field of neuro-oncology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.701-720
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• 2023

Aerial objects are more challenging to segment than normal objects, which are usually smaller and have less textural detail. In the process of segmentation, target objects are easily omitted and misdetected, which is problematic. To alleviate these issues, we propose local aggregation feature pyramid networks (LAFPNs) and pyramid integrated context modules (PICMs) for aerial object segmentation. First, using an LAFPN, while strengthening the deep features, the extent to which low-level features interfere with high-level features is reduced, and numerous dense and small aerial targets are prevented from being mistakenly detected as a whole. Second, the PICM uses global information to guide local features, which enhances the network's comprehensive understanding of an entire image and reduces the missed detection of small aerial objects due to insufficient texture information. We evaluate our network with the MS COCO dataset using three categories: airplanes, birds, and kites. Compared with Mask R-CNN, our network achieves performance improvements of 1.7%, 4.9%, and 7.7% in terms of the AP metrics for the three categories. Without pretraining or any postprocessing, the segmentation performance of our network for aerial objects is superior to that of several recent methods based on classic algorithms.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1995년도 학술대회
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• pp.97-100
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• 1995

In transmission of image sequences over ATM networks, the packet loss problem and channel sharing efficiency are important. As a possible solution two-layer video coding methods have been proposed. These methods transmit video information over the network with different levels of protection with respect to packets loss. In this paper, a two-layer coding method using pyramid structure is proposed and several realizations of two-layer video coding methods are presented and their performances are compared.