The exponential increase of digital data and the limited capacity of current storage devices have made clear the need for exploring new storage solutions. Thanks to its biological properties, DNA has proven to be a po...
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ISBN:
(纸本)9781728185514
The exponential increase of digital data and the limited capacity of current storage devices have made clear the need for exploring new storage solutions. Thanks to its biological properties, DNA has proven to be a potential candidate for this task, allowing the storage of information at a high density for hundreds or even thousands of years. With the release of nanopore sequencing technologies, DNA data storage is one step closer to become a reality. Many works have proposed solutions for the simulation of this sequencing step, aiming to ease the development of algorithms addressing nanopore-sequenced reads. However, these simulators target the sequencing of complete genomes, whose characteristics differ from the ones of synthetic DNA. This work presents a nanopore sequencing simulator targeting synthetic DNA on the context of DNA data storage.
In this paper, we present the implementation of the robust detail preserving filters with complex noise suppression for imageprocessing applications. The designed filter is the consequential connection of two filters...
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ISBN:
(纸本)0819444111
In this paper, we present the implementation of the robust detail preserving filters with complex noise suppression for imageprocessing applications. The designed filter is the consequential connection of two filters. The first filter uses the value of central pixel of the filtering window to provide the preservation of fine details and the redescending M-estimators combined with the median estimator to provide impulsive noise rejection. The second filter uses the output of the first filter as the pre-estimator for an adaptive calculation in the redescending M-estimator. We investigated various types of influence functions in the M-estimator those are similar to the ones used in the Sigma filter to provide multiplicative noise suppression. The optimal values of the parameters of designed filters in presence of different noise mixture are determined. Different simulation data are presented in the paper and shown the statistical efficiency of the filters.
In stereo image super-resolution (SR), it is equally important to utilize intra-view and cross-view information. However, most existing methods only focus on the exploration of cross-view information and neglect the f...
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ISBN:
(纸本)9781728185514
In stereo image super-resolution (SR), it is equally important to utilize intra-view and cross-view information. However, most existing methods only focus on the exploration of cross-view information and neglect the full mining of intra-view information, which limits the reconstruction performance of these methods. Since single image SR (SISR) methods are powerful in intra-view information exploitation, we propose to introduce the knowledge distillation strategy to transfer the knowledge of a SISR network (teacher network) to a stereo image SR network (student network). With the help of the teacher network, the student network can easily learn more intra-view information. Specifically, we propose pixel-wise distillation as the implementation method, which not only improves the intra-view information extraction ability of student network, but also ensures the effective learning of cross-view information. Moreover, we propose a lightweight student network named Adaptive Residual Feature Aggregation network (ARFAnet). Its main unit, the ARFA module, can aggregate informative residual features and produce more representative features for image reconstruction. Experimental results demonstrate that our teacher-student network achieves state-of-the-art performance on all benchmark datasets.
Compressed image quality assessment (IQA) has been a crucial part of a wide range of image services such as storage and transmission. Due to the effect of different bit rates and compression methods, the compressed im...
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ISBN:
(纸本)9781728185514
Compressed image quality assessment (IQA) has been a crucial part of a wide range of image services such as storage and transmission. Due to the effect of different bit rates and compression methods, the compressed images usually have different levels of quality. Nowadays, the mainstream full-reference (FR) metrics are effective to predict the quality of compressed images at coarse-grained levels, however, they may perform poorly when quality differences of the compressed images are quite subtle. To better improve the Quality of Experience (QoE) and provide useful guidance for compression algorithms, we propose an FR-IQA metric for fine-grained compressed images, which estimates the image quality by analyzing the difference of structure and texture. Our metric is mainly validated on the fine-grained compression IQA (FGIQA) database and is tested on other commonly used compression IQA databases as well. The experimental results show that our metric outperforms mainstream FR-IQA metrics on the fine-grained compression IQA database and also obtains competitive performance on the coarse-grained compression IQA databases.
Recent advancements in learned image compression methods have demonstrated superior rate-distortion performance and remarkable potential compared to traditional compression techniques. However, the core operation of q...
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ISBN:
(纸本)9798331529543;9798331529550
Recent advancements in learned image compression methods have demonstrated superior rate-distortion performance and remarkable potential compared to traditional compression techniques. However, the core operation of quantization, inherent to lossy image compression, introduces errors that can degrade the quality of the reconstructed image. To address this challenge, we propose a novel Quantization Error Compensator (QEC), which leverages spatial context within latent representations and hyperprior information to effectively mitigate the impact of quantization error. Moreover, we propose a tailored quantization error optimization training strategy to further improve rate-distortion performance. Notably, QEC serves as a lightweight, plug-and-play module, offering high flexibility and seamless integration into various learned image compression methods. Extensive experimental results consistently demonstrate significant coding efficiency improvements achievable by incorporating the proposed QEC into state-of-the-art methods, with a slight increase in runtime.
Synthesizing images from text is an important problem and has various applications. Most of the existing studies of text-to-image generation utilize supervised methods and rely on a fully-labeled dataset, but detailed...
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ISBN:
(纸本)9781728180687
Synthesizing images from text is an important problem and has various applications. Most of the existing studies of text-to-image generation utilize supervised methods and rely on a fully-labeled dataset, but detailed and accurate descriptions of images are onerous to obtain. In this paper, we introduce a simple but effective semi-supervised approach that considers the feature of unlabeled images as " Pseudo Text Feature". Therefore, the unlabeled data can participate in the following training process. To achieve this, we design a Modality-invariant Semanticconsistent Module which aims to make the image feature and the text feature indistinguishable and maintain their semantic information. Extensive qualitative and quantitative experiments on MNIST and Oxford-102 flower datasets demonstrate the effectiveness of our semi-supervised method in comparison to supervised ones. We also show that the proposed method can be easily plugged into other visual generation models such as image translation and performs well.
Uniform scalar quantizers are widely used in image coding. They are known to be optimum entropy constrained scalar quantizers within the high resolution assumption. In this paper, we focus on the design of nearly unif...
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ISBN:
(纸本)0819444111
Uniform scalar quantizers are widely used in image coding. They are known to be optimum entropy constrained scalar quantizers within the high resolution assumption. In this paper, we focus on the design of nearly uniform scalar quantizers for high performance coding of wavelet coefficients whatever the bitrate is. Some codecs use uniform scalar quantizers with a zero quantization bin size (deadzone) equal to two times the other quantization bin sizes (for example JPEG2000). We address the problem of deadzone size optimization using distortion rate considerations. The advantages of the proposed method are that the quantizer design is adapted to both the source statistics and the compression ratio. Our method is based on statistical information of the wavelet coefficients distribution. It provides experimental gains up to 0.19 dB.
Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches. We propose a novel NN-based image coding...
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ISBN:
(纸本)9781665475921
Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches. We propose a novel NN-based image coding framework that utilizes forward wavelet transforms to decompose the input signal by spatial frequency. Our encoder generates separate bitstreams for each latent representation of low and high frequencies. This enables our decoder to selectively decode bitstreams in a quality-scalable manner. Hence, the decoder can produce an enhanced image by using an enhancement bitstream in addition to the base bitstream. Furthermore, our method is able to enhance only a specific region of interest (ROI) by using a corresponding part of the enhancement latent representation. Our experiments demonstrate that the proposed method shows competitive rate-distortion performance compared to several non-scalable image codecs. We also showcase the effectiveness of our two-level quality scalability, as well as its practicality in ROI quality enhancement.
This paper used Time-Frequency Analysis (TFA) techniques for signal processing on tasks of computer vision. Our main idea is as follows: To build a simple network architecture without two or more convolutional neural ...
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ISBN:
(纸本)9781665475921
This paper used Time-Frequency Analysis (TFA) techniques for signal processing on tasks of computer vision. Our main idea is as follows: To build a simple network architecture without two or more convolutional neural networks (CNNs), analyze hidden features by Discrete Wavelet Transform (DWT), and send them into filters as weights by convolutions, transformers or other methods. And we do not need to build the network with 2 or more stages to accomplish this idea. Actually, we try to directly use TFA skills on CNN to build one-stage network. Networks which build by this way not only keep their outstanding performance, but also cost lower computing resources. In this paper, we mainly use DWT on CNN to solve image inpainting problems. And the results show that our model can work stably in frequency domain to realize free-form image inpainting.
In this paper, we proposed an optimized model based on the visual attention mechanism(VAM) for no-reference stereoscopic image quality assessment (SIQA). A CNN model is designed based on dual attention mechanism (DAM)...
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ISBN:
(纸本)9781728180687
In this paper, we proposed an optimized model based on the visual attention mechanism(VAM) for no-reference stereoscopic image quality assessment (SIQA). A CNN model is designed based on dual attention mechanism (DAM), which includes channel attention mechanism and spatial attention mechanism. The channel attention mechanism can give high weight to the features with large contribution to final quality, and small weight to features with low contribution. The spatial attention mechanism considers the inner region of a feature, and different areas are assigned different weights according to the importance of the region within the feature. In addition, data selection strategy is designed for CNN model. According to VAM, visual saliency is applied to guide data selection, and a certain proportion of saliency patches are employed to fine tune the network. The same operation is performed on the test set, which can remove data redundancy and improve algorithm performance. Experimental results on two public databases show that the proposed model is superior to the state-of-the-art SIQA methods. Cross-database validation shows high generalization ability and high effectiveness of our model.
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