Context‐based compression of binary images in parallel

Binary images can be compressed efficiently using context‐based statistical modeling and arithmetic coding. However, this approach is fully sequential and therefore additional computing power from parallel computers cannot be utilized. We attack this problem and show how to implement the context‐based compression in parallel. Our approach is to segment the image into non‐overlapping blocks, which are compressed independently by the processors. We give two alternative solutions about how to construct, distribute and utilize the model in parallel, and study the effect on the compression performance and execution time. We show by experiments that the proposed approach achieves speedup that is proportional to the number of processors. The work efficiency exceeds 50% with any reasonable number of processors. Copyright © 2002 John Wiley & Sons, Ltd.     

基于信号特征的雷达图像无损压缩算法

提出了一个有效的在VDR中记录雷达图像的无损压缩算法。该算法基于对雷达信号特征的分析，充分挖掘其中的各种信息冗余进行压缩编码。首先进行帧间预测编码，并解决了可能出现的误差累积问题。在帧内预测编码时，提出了沿最小梯度方向进行预测的分类预测器。在熵编码阶段，提出了若干图像序列共享概率模型的方法。     

Analysis of non-local image denoising methods

Image denoising is probably one of the most studied problems in the image processing community. Recently a new paradigm on non-local denoising was introduced. The non-local means method proposed by Buades, Morel and Coll computes the denoised image as a weighted average of pixels across the whole image. The weight between pixels is based on the similarity between neighborhoods around them. This method attracted the attention of other researchers who proposed improvements and modifications to it. In this work we analyze those methods trying to understand their properties while connecting them to segmentation based on spectral properties of the graph that represents the similarity of neighborhoods of the image. We also propose a method to automatically estimate the parameters which produce the optimal results in terms of mean square error and perceptual quality.     

Forward‐adaptive method for context‐based compression of large binary images

A method for compressing large binary images is proposed for applications where spatial access to the image is required. The proposed method is a two‐stage combination of forward‐adaptive modeling and backward‐adaptive context based compression with re‐initialization of statistics. The method improves compression performance significantly in comparison to a straightforward combination of JBIG and tiling. Only minor modifications to the QM‐coder are required, and therefore existing software implementations can be easily utilized. Technical details of the modifications are provided. Copyright © 1999 John Wiley & Sons, Ltd.     

Anisotropic diffusion based denoising on X-radiography images to detect weld defects

This paper proposes a machine vision scheme for denoising, feature space gradient preserving, and detecting weld defects in noisy weld X-radiography images; particularly, for the images that are in low-contrast and contain noises. The detection of small weld defects present on noisy image is extremely difficult in non-destructive testing through machine vision. The presence of high gradient magnitude and the low intensity in the feature space of a noisy image are the main characteristics of weld defects. These characteristics can be considered to refine and obtain noise-free images for detection of weld defects. This study proposes a modified anisotropic diffusion model, which considers a local probability value of gray-level and an adaptive threshold parameter in diffusion coefficient function to adjust the implication of low edge gradient of the feature space from the noisy image. Furthermore, an entropy based stopping criterion has been introduced to terminate the diffusion process. This proposed model is compared with the existing models, and its performance is evaluated through Mean Square Error (MSE), Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Entropy (E) and Mean Structural Similarity (MSSIM) measures. Experimental results confirm the reliability of the proposed model.     

Lossless compression and information hiding in images

This paper presents an image lossless compression and information-hiding schemes based on the same methodology. The methodology presented here is based on the known SCAN formal language for data accessing and processing. In particular, the compression part produces a lossless compression ratio of 1.88 for the standard Lenna, while the hiding part is able to embeds digital information at 12.5% of the size of the original image. Results from various images are also provided.     

一种基于聚类和统计分析DNA基因芯片图像处理算法

DNA基因芯片可以同时监控成千上万个基因的表达信息。图像分析是基因芯片试验中一个重要的环节,直接影响到其后续的处理、分析和研究,比如鉴别预测具有不同表达信息的基因功能。基因芯片图像分析包括三个步骤:图像网格化,图像分割以及信息抽取。该文主要研究分割和信息抽取问题。首先基于K-Means聚类技术提出了一种新的分割方法;其次基于统计分析文章建议了一种新的背景和前景分割校正方法用于更准确的信息抽取。新方法的优点是对于基因芯片中spot图像没有任何形状限制。实际图像分析结果与目前最流行的基因芯片图像分析软件GenePix对比研究表明该文算法是精确有效的。     

Robust adaptive spot segmentation of DNA microarray images

The rapid advancement of DNA chip (microarray) technology has revolutionalized genetic research in bioscience. However, the enormous amount of data produced from a microarray image makes automatic computer analysis indispensable. An important first step in analyzing microarray image is the accurate determination of the DNA spots in the image. We report here a novel spot segmentation method for DNA microarray images. The algorithm makes use of adaptive thresholding and statistical intensity modeling to: (i) generate the grid structure automatically, where each subregion in the grid contains only one spot, and (ii) to segment the spot, if any, within each subregion. The algorithm is fully automatic, robust, and can aid in the high throughput computer analysis of microarray data.     

基于Transformer和通道混合并行卷积的高光谱图像去噪

目的 高光谱图像因设备及环境因素容易受到噪声污染,导致图像的可见性和分析精度降低,因此高光谱图像去噪任务已经成为遥感图像处理领域国内外研究热点。当前的高光谱图像去噪方法主要面临两个难题：1）对特征的全局信息利用不足。当前基于卷积神经网络的方法受限于卷积核的大小,难以捕获特征的全局信息;2）卷积神经网络和 Transformer 在结构上存在差异,导致两者难以融合,因此,需要考虑合理的特征交互方式,来平衡局部和全局特征提取之间的关系。方法 针对上述问题,本文提出了基于 Transformer 和通道混合并行卷积的高光谱图像去噪模型,包括 3 个模块：通道混合特征提取模块、基于块下采样的全局增强模块和自适应双向特征融合模块。通过这 3 个模块的相互作用,可以充分结合全局和局部的特征信息,处理不同区域中的噪声和纹理差异,有效提高模型对空间细节信息的恢复能力。结果 实验在 2 个数据集上与主流的 5 种方法进行比较,在 Pavia 数据集中设置不同高斯噪声强度的情况下,相比于性能第 2 的模型,峰值信噪比（peak signal-to-noise ratio,PSNR）值最大提高了0. 4 dB;在 ICVL 数据集中设置各种混合噪声的情况下,相比于性能第 2 的模型,PSNR 最大提高了 2. 18 dB。同时可视化的去噪结果图像体现了本文所提出的去噪模型的优异性能。结论 本文方法在各种噪声情况下均具有较好的去噪效果,显著优于当前主流方法,能够有效去除高光谱图像中噪声,同时保留图像丰富的纹理信息。     

基于DPCM与Hilbert扫描的灰度图像无损压缩方法

提出一种基于DPCM与Hilbert曲线的医疗图像无损压缩方法,通过差分脉码调制技术（DPCM）对图像进行预测处理,得到差值图像,再利用Hilbert曲线对医疗图像像素的进行扫描,得到图像的一维数据,然后分别用哈夫曼编码、游程编码和字典编码对一维数据进行压缩。实验结果显示Hilbert扫描可以增加像素的相关性,对提高压缩比有一定的贡献。     

Region oriented compression of color images using fuzzy inference and fast merging

A new region based lossy compression scheme for color images is proposed. The segmentation method belongs to the split and merge category. Splitting is carried out using the watershed transform. In the merging stage, a fuzzy color preserving rule-based system and a novel one-dimensional graph structure are introduced to provide accurate results with reduced computational complexity. The compression part is based on the Shape Adaptive DCT with ΔDC correction method. The quantization matrices used have been designed according to the properties of the employed transform. Promising perceptual results for the low bit rate range compared to previously reported compression methods have also been reported.     

A new wavelet-based fuzzy single and multi-channel image denoising

In this paper, we propose a new wavelet shrinkage algorithm based on fuzzy logic. In particular, intra-scale dependency within wavelet coefficients is modeled using a fuzzy feature. This feature space distinguishes between important coefficients, which belong to image discontinuity and noisy coefficients. We use this fuzzy feature for enhancing wavelet coefficients' information in the shrinkage step. Then a fuzzy membership function shrinks wavelet coefficients based on the fuzzy feature. In addition, we extend our noise reduction algorithm for multi-channel images. We use inter-relation between different channels as a fuzzy feature for improving the denoising performance compared to denoising each channel, separately. We examine our image denoising algorithm in the dual-tree discrete wavelet transform, which is the new shiftable and modified version of discrete wavelet transform. Extensive comparisons with the state-of-the-art image denoising algorithm indicate that our image denoising algorithm has a better performance in noise suppression and edge preservation.     

形态小波域声呐图像谱聚类去噪算法研究

针对声呐图像噪声污染严重的问题,在基于形态小波的声呐图像去噪方法中引入了谱聚类算法以实现低信噪比下图像的去噪.给出基于形态中点小波的声呐图像去嗓法,在此基础上引入谱聚类的概念,针对谱聚类能快速实现数据分类的特点,对形态中点小波分解后的高频小波系数进行分类,使得包含噪声与细节信号部分的小波系数得以分离.对分离后的两类小波...     

A new feature-preserving nonlinear anisotropic diffusion for denoising images containing blobs and ridges

Blobs and ridges underlie many important features in biological, biometric and remote sensing images. These images are likely to be corrupted by noise, such as live cells in fluorescent biological images, ridges and valleys in fingerprints and moving targets in synthetic aperture radar and infrared images. In this paper we present a diffusion method for denoising low-signal-to-ratio images containing blob and ridge features. A commonly used denoising method makes use of edge information in an image to achieve a good balance between noise removal and feature preserving. However, if edges are partly lost to a certain extent or contaminated severely by noise, such an approach may not be able to preserve these features, leading to loss of important information. To overcome this problem, we propose a novel second-order nonlocal derivative as a robust blob and ridge detector and incorporate it into a diffusion process to form a novel feature-preserving nonlinear anisotropic diffusion model. Experiments show that the new diffusion filter outperforms many popular filters for preserving blobs and ridges, reducing noise and minimizing artifacts.     

A hierarchical refinement algorithm for fully automatic gridding in spotted DNA microarray image processing

Gridding, the first step in spotted DNA microarray image processing, usually requires human intervention to achieve acceptable accuracy. We present a new algorithm for automatic gridding based on hierarchical refinement to improve the efficiency, robustness and reproducibility of microarray data analysis. This algorithm employs morphological reconstruction along with global and local rotation detection, non-parametric optimal thresholding and local fine-tuning without any human intervention. Using synthetic data and real microarray images of different sizes and with different degrees of rotation of subarrays, we demonstrate that this algorithm can detect and compensate for alignment and rotation problems to obtain reliable and robust results.     

Research on denoising and enhancing methods of medical images based on convolutional neural networks

In the process of modern medical diagnosis, medical image-assisted diagnosis plays a very important role. However, the process of medical image acquisition, will be affected by various types and degrees of noise, and there will be a certain probability of producing strip artifacts, which will interfere with the doctor's diagnosis, analysis, and treatment of diseases to a certain extent. However, the traditional medical image denoising method will cause problems such as image edge blurring and detail loss, and it is difficult to achieve the balance between noise removal and detail information retention. Therefore, denoising medical images and improving the accuracy of denoising as much as possible have very important scientific research significance and clinical application value. Based on this, this article proposes a medical image denoising method based on a double residual convolutional neural network and compares it with traditional medical images denoising methods such as K-SVD, BM3D, and PNLM3. Experimental results show that the medical image denoising method based on the double residual convolutional neural network proposed in this article has excellent performance.     

基于小波变换的改进DnCNN网络的遥感图像去噪

遥感图像去噪一直是遥感领域的重要难题,现有的去噪算法会使图像边缘信息模糊,导致图像中有用信息丢失,为了提高遥感图像的质量,提出了一种改进DnCNN（Denoising Convolutional Neural Network）的遥感图像去噪方法,通过小波变换将原始图像分解成不同子带,采用基于遗传算法的网络结构自动搜索方法对于不同子带搜索出不同结构和参数的DnCNN网络实现去噪,使对噪声成分的提取更加有针对性。实验采用峰值信噪比（PSNR）和结构相似性（SSIM）两项评价指标对实验结果进行量化评判,标准差为20时,较原始的DnCNN方法相比PSNR值平均提高了3.5%,图像细节清晰,能有效地保护遥感图像边缘特征和轮廓结构的完整性。     

Robust watermarking and compression for medical images based on genetic algorithms

A ROI (region of interest) of a medical image is an area including important information and must be stored without any distortion. In order to achieve optimal compression as well as satisfactory visualization of medical images, we compress the ROI by lossless compression, and the rest by lossy compression. Furthermore, security is an important issue in web-based medical information system. Watermarking skill is often used for protecting medical images. In this paper, we present a robust technique embedding the watermark of signature information or textual data around the ROI of a medical image based on genetic algorithms. A fragile watermark is adopted to detect any unauthorized modification. The embedding of watermark in the frequency domain is more difficult to be pirated than in spatial domain.     

Compression and classification methods for hyperspectral images

In this article we present new lossless compression methods by combining existing methods and compare them using AVIRIS images. These methods include the Self-Organizing Map (SOM), Principal Component Analysis (PCA), and the three-dimensional Wavelet Transform combined with traditional lossless encoding methods. The two-dimensional JPEG2000 and SPIHT compression methods were applied to the eigenimages produced by the PCA. The bit allocation for the compression of eigenimages was based on the amount of information in each eigenimage. In bit rate calculation we used the exponential entropy formula, which gave better results than the original linear version. The information loss from the compression was measured by the Signal-to-Noise Ratio (SNR) and Peak-Signal-to-Noise Ratio (PSNR). To get more illustrative and practical error measures, classification of spectra was performed using unsupervised K-means clustering combined with spectral matching. Spectral matching methods include Euclidean distance, Spectral Similarity Value (SSV), and Spectral Angle Mapper (SAM). We used two test images, which both were AVIRIS images with 224 bands and 512 lines in 614 columns. The PCA in the spectral dimension combined with JPEG2000 or SPIHT in the spatial dimension was the best method in terms of the image quality and compression speed.     

基于稀疏分解和聚类的自适应图像去噪新方法

随着信号稀疏表示原理的深入研究,稀疏分解越来越广泛地应用于图像处理领域。针对过完备字典构造和稀疏分解运算量巨大的问题,提出一种基于稀疏分解和聚类相结合的自适应图像去噪新方法。该方法首先通过改进的K均值(K-means)聚类算法训练样本,构造过完备字典;其次,通过训练过程中每一次迭代,自适应地更新字典的原子,使字典更适应样本的稀疏表示;然后利用正交匹配追踪(OMP)算法实现图像的稀疏表示,从而达到图像去噪的目的。实验结果表明：与传统的字典训练方法相比,新算法有效地降低了运算复杂度,并取得更好的图像去噪效果。