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An Effective Adaptive Fuzzy Filter for SAR image Noise Reduction

An Effective Adaptive Fuzzy Filter for SAR Image Noise Reduc...

引用

2023 IEEE Global conference on Information Technologies and communications, GCITC 2023

作者： Kushwaha, Sumit University Institute of Computing Chandigarh University Department of Computer Applications India

ISBN: (纸本)9798350308167

In this paper, I propose a fuzzy logic methodology for the problem of uncertainty in an innovative manner. The foundation of this strategy is synthetic aperture radar, which can provide high-resolution images in any conditions, day or night. Speckle noise, which is produced by the random interference of EM signals disseminated by the object's plane within one resolution group, distorts the SAR images, but application scientists have widely accepted and utilized them. It is impossible to predict how much visual cacophony will appear or where its concentration will be. The quantitative nature of the majority of extant noise filters disqualifies them for use with uncertain data. According to this study, non-sub sampling filter banks may effect the transform's directionality, anisotropy, and translation invariance. This filter has been demonstrated to effectively reduce noise while preserving image edge quality. © 2023 IEEE.

关键词： Fuzzy logic image processing Noise Reduction SAR image Speckle Noise

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Baker’s Map Based Chaotic image Encryption in Military Surveillance Systems 1

引用

International conference on Internet of Everything and Quantum Information processing, IEQIP 2023

作者： Rajendiran, Nithya Paranthaman Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

One of the primary problems in military surveillance systems is the safety of the visual data gathered by the sensor nodes installed in the different areas of the visual sensor network and the remotely operated devices. There are several cryptographic algorithms available to secure the visual information. Several chaotic schemes worked well because they were more resistant to threats and had greater entropy levels. But, it takes a lot of time to finish the encryption step. Traditional approaches had the drawback that increasing encryption strength slowed calculation speed. This paper presents a scheme called "baker’s map chaotic method based image encryption" to accelerate the encryption process in a network of visual sensors without weakening the encryption method. The findings indicate that the system’s functionality is improved when the computation time is reduced to sixty seconds and the PSNR is increased to 15 dB. The performance of the chaotic method is compared by using key space analysis, key sensitivity analysis, correlation coefficient analysis, noise analysis, Entropy analysis, and Speed analysis. The IBM SPSS statistical tool was also used to do statistical analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sensitivity analysis

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Preparing VVC for Streaming: A Fast Multi-Rate Encoding Approach

Preparing VVC for Streaming: A Fast Multi-Rate Encoding Appr...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Liu, Yiqun Amirpour, Hadi Abdoli, Mohsen Timmerer, Christian Guionnet, Thomas Ateme Rennes France Alpen-Adria-Universität Christian Doppler Laboratory ATHENA Klagenfurt Austria IRT Bcom Cesson-Sévigné France

ISBN: (纸本)9798350359855

The integration of advanced video codecs into the streaming pipeline is growing in response to the increasing demand for high quality video content. However, the significant computational demand for advanced codecs like Versatile Video Coding (VVC) poses challenges for service providers, including longer encoding time and higher encoding cost. This challenge becomes even more pronounced in streaming, as the same content needs to be encoded at multiple bitrates (also known as representations) to accommodate different network conditions. To accelerate the encoding process of multiple representations of the same content in VVC, we employ the encoding map of a single representation, known as the reference representation, and utilize its partitioning structure to accelerate the encoding of the remaining representations, referred to as dependent representations. To ensure compatibility with parallel processing, we designate the lowest bitrate representation as the reference representation. The experimental results indicate a substantial improvement in the encoding time for the dependent representations, achieving an average reduction of 40%, while maintaining a minimal average quality drop of only 0.43 in Video Multi-method Assessment Fusion (VMAF). This improvement is observed when utilizing Versatile Video Encoder (VVenC), an open and optimized VVC encoder implementation. © 2023 IEEE.

关键词： Video signal processing

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Micro-Spatial Attention with Sparse Constraint for Self-Supervised Learning for Oleaginous Yeast image Representation

Micro-Spatial Attention with Sparse Constraint for Self-Supe...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Ohtomo, Kazuma Kitahara, Yukina Harakawa, Ryosuke Nakamura, Akihiro Shida, Yosuke Ogasawara, Wataru Iwahashi, Masahiro Nagaoka University of Technology Department of Information Science and Control Engineering Japan Nagaoka University of Technology Department of Electrical Electronics and Information Engineering Japan Nagaoka University of Technology Department of Science of Technology Innovation Japan Nagaoka University of Technology Department of Bioengineering Japan

ISBN: (纸本)9798350359855

Feature representation of oleaginous yeast images is essential for non-invasively observing their cultivation status. However, conventional methods are difficult to capture the visual characteristics of oleaginous yeasts at the extremely local spatial regions. To tackle this problem, this paper proposes micro-spatial attention with sparse constraints for self-supervised learning. Specifically, we newly design a micro-spatial attention module to focus on the extremely local spatial regions for feature representation. Unlike the existing spatial attention module, which requires the guide information (e.g., labels and textual queries) to focus on specific regions, our proposed module employs a sparse constraint. By combining the sparse constraint with self-supervised learning, we can automatically focus on essential regions, which appear in extremely local regions. This improves the feature representation power for oleaginous yeast images with micro-spatial contexts. Experiments on real Lipomyces starkeyi datasets, a specific type of oleaginous yeasts, confirm the superiority of our method. Moreover, our method realized clustering of oleaginous yeast images, enabling us to non-invasively observe the cultivation trends over time. © 2023 IEEE.

关键词： image enhancement

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Textual Training for the Hassle-Free Removal of Unwanted visual Data: Case Studies on OOD and Hateful image Detection 38

Textual Training for the Hassle-Free Removal of Unwanted Vis...

引用

38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Lee, Saehyung Mok, Jisoo Park, Sangha Shin, Yongho Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Qualcomm Korea YH Seoul Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT. © 2024 Neural information processing systems foundation. All rights reserved.

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Lightweight image Classification Algorithm Based on Dual Knowledge Distillation 6

Lightweight Image Classification Algorithm Based on Dual Kno...

引用

6th International conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Xiao, Peng Liu, Ying Hao, Yu Center for Image and Information Processing School of Communications and Information Engineering Xi'an University of Posts and Telecommunications China Center for Image and Information Processing School of Communications and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Deep learning models perform exceptionally well in image processing;however, the increasing number of parameters and computational burdens make it difficult to apply these models on mobile devices. To address this issue, a widely adopted approach is using knowledge distillation to compress and optimize deep learning models. Traditional knowledge distillation trains a student network with soft targets from a teacher network. This paper proposes a Novel Dual Knowledge Distillation (NDKD) network by integrating Convolutional Block Attention Module (CBAM) distillation and feature distillation. Specifically, a new CBAM module and a hash layer are first added to improve the teacher-student network. Next, attention distillation loss is computed to transfer CBAM knowledge from the teacher module to the student network to enhance the student network's ability to capture low-level visual features. Finally, feature distillation loss is constructed by hashing layers to transfer semantic knowledge from the teacher network to the student network to enhance the student network's high-level semantic ability. Our method is compared with other knowledge distillation methods on benchmarking datasets including the CIFAR-10, SVNM, EMNIST, and Tire Pattern image dataset CIIP-TPID. Experimental results show that our method outperforms other methods on adapted datasets. © 2023 ACM.

关键词： Distillation

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A Novel Learnable Orthogonal Wavelet Unit Neural Network with Perfection Reconstruction Constraint Relaxation for image Classification

A Novel Learnable Orthogonal Wavelet Unit Neural Network wit...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Le, An D Jin, Shiwei Bae, You Suk Nguyen, Truong University of California San Diego Jacobs School of Engineering California United States Technology University of Korea Department of Computer Engineering Gyeonggi-do Korea Republic of

ISBN: (纸本)9798350359855

CNNs utilize lowpass frequency features, evidenced by max pooling operations that preserve dominant features. Addressing this, we used a one-layer FCN using both coarse and detailed components via DWT. We then developed UwU, a learnable wavelet-based unit that integrates the PR constraint relaxation, allowing feature map component fine-Tuning. Distinctively, UwU's coefficients are trainable, unlike prior studies. This is the first work that utilizes PR constraint relaxation to enhance CNNs. Our innovative techniques serve to enhance stride-convolution, pooling, and downsampling units in CNNs. We tested these improved units using the ResNet family architectures against traditional frequency and wavelet-based units. Performance metrics from CIFAR10, imageNet1K, and the DTD show promising results. Particularly, while CIFAR10 results are on par with other methods, there's a marked performance improvement on imageNet1K and DTD. Further, integrating UwU into the ResNet18-based encoder of the CFLOW-AD system yields competitive anomaly detection results, particularly for hazelnut images in the MVTecAD dataset. © 2023 IEEE.

关键词： Wavelet transforms

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Learned image Compression with Enhanced Dynamic Spatial Aggregation and Asymmetric Entropy model

Learned Image Compression with Enhanced Dynamic Spatial Aggr...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Zhang, Yuanton Fu, Nianxiang Yu, Guo Lv, Xiangdong Wang, Huairui Chen, Zhenzhong Wuhan University School of Remote Sensing and Information Engineering Wuhan China Wuhan University Hubei Luojia Laboratory Wuhan China Wuhan University School of Computer Science Wuhan China

ISBN: (纸本)9798350359855

Learned image compression (LIC) has shown significant potential and better rate-distortion performance than traditional techniques. However, existing CNN-based approaches or window-based self-Attention methods can only capture spatial information within fixed ranges. To tackle this limitation, we propose a novel method with dynamic spatial aggregation for transform coding. Our approach introduces enhanced adaptive aggregation, which generates kernel offsets to capture relevant information within content-dependent ranges, improving the transform process. Furthermore, we define a generalized coarse-To-fine entropy model that considers global context, channel-wise information, and spatial context in a coarse-To-fine manner. Additionally, our method takes into full consideration the model's efficiency and complexity. By introducing the asymmetric entropy model structure and efficient heterogeneous convolution, our approach maintains lower coding complexity and higher decoding speed while ensuring performance. Experimental results demonstrate a substantial improvement in rate-distortion performance achieved by our method when compared to traditional compression methods like VTM and BPG, as well as some LIC methods. © 2023 IEEE.

关键词： image compression

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Hybrid Implicit Neural image Compression with Subpixel Context Model and Iterative Pruner

Hybrid Implicit Neural Image Compression with Subpixel Conte...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Li, Hangyu Jiang, Wei Li, Litian Zhai, Yongqi Wang, Ronggang Peking University Shenzhen Graduate School Shenzhen China Pengcheng Laboratory Shenzhen China

ISBN: (纸本)9798350359855

Recently, image compression based on Implicit Neural Representation (INR) has gained interest. However, the performance of INR-based methods is poor because MLP can not fit high-frequency information well. Recently, a kind of hybrid INR method was proposed, which transmits not only the parameters of MLP but also some external latents to help to overfit at the decoder. Despite its low MACs (Multiply-Accumulate Operations per pixel), the decoding time is still slow due to the serial autoregressive context model for entropy coding. Hence, we propose two methods to improve the hybrid INR method's decoding speed and performance. First, the Subpixel Context Model (SAM) is aimed to speed up the process of entropy coding. The proposed SAM is latent by latent instead of pixel by pixel. Second, we add an iterative pruner to the pipeline of hybrid INR compression, to further reduce the bit rate. The performance and MACs of our approach are on par with the SOTA of the hybrid INR methods. The decoding speed is about eight times faster on CPU, and three times faster on GPU. © 2023 IEEE.

关键词： image compression

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Underwater image Restoration Based on An Improved Underwater image Formation Model 14

Underwater Image Restoration Based on An Improved Underwater...

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14th IEEE International conference on Wireless communications and Signal processing (WCSP)

作者： Fan, XinNan Zhou, Xuan Shi, PengFei Sun, HuanRu Ye, ShuYue Hohai Univ Coll Internet Things Engn Changzhou Peoples R China

ISBN: (纸本)9781665450850

Underwater images often show characteristics of low contrast, low visibility and color distortion. To improve underwater image quality, a novel underwater image restoration algorithm is proposed in this paper. First of all, we put forward an underwater image restoration network (UIRnet) based on CNN(Convolutional Neural Network) for transmission estimation in the underwater imaging model. Besides, the Leaky ReLU(Rectified Linear Units) activation function and SENet(Squeeze-and-Excitation Networks) unit are used to improve the ability of extracting image features and training parameters. Secondly, the fuzzy patch dataset which is used as input dataset is synthesized by the underwater optical imaging model and randomly generated transmission. Meanwhile, the corresponding transmission value is used as label dataset. Then, we use the dataset to train and optimize all parameters in the UIRnet. Finally, the trained UIRnet is used in underwater image restoration. The experimental results show that the proposed algorithm can restore the details of the image, improve the contrast, and have a more natural visual effect.

关键词： image restoration image formation model Underwater images

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