UC知道请帮忙翻译一下，关于图像噪声的论文，谢谢
来源：百度知道 编辑：UC知道 时间：2024/05/30 06:16:29
请尽量语言连贯，翻译的好有分追加，谢谢！
I. INTRODUCTION
Image is often corrupted by noise in its acquisition and transmission. For example during the image acquisition,the performance of imaging sensors is affected by a variety of factors, such as environmental conditions and by the quality of the sensing elements themselves. For instance, in acquiring images with a CCD camera, light levels and sensor temperature are major factors affecting the amount of noise in the resulting image. Images are also corrupted during transmission, due to interference in the channel used for transmission. Image denoising techniques are necessary to remove such random additive noises while retaining as much as possible the important signal features. The main objective of these types of random noise removal is to suppress the noise while preserving the original image details. Statistical filters like Average filter [1] [2], Wiener filter [3] can be used for removing such noises but the wavelet based denoising techn
I. INTRODUCTION
Image is often corrupted by noise in its acquisition and transmission. For example during the image acquisition,the performance of imaging sensors is affected by a variety of factors, such as environmental conditions and by the quality of the sensing elements themselves. For instance, in acquiring images with a CCD camera, light levels and sensor temperature are major factors affecting the amount of noise in the resulting image. Images are also corrupted during transmission, due to interference in the channel used for transmission. Image denoising techniques are necessary to remove such random additive noises while retaining as much as possible the important signal features. The main objective of these types of random noise removal is to suppress the noise while preserving the original image details. Statistical filters like Average filter [1] [2], Wiener filter [3] can be used for removing such noises but the wavelet based denoising techn
导言
损坏的图像往往是在其噪声采集和传输。例如在图像采集，其性能的影像传感器是受多种因素，如环境条件和质量检测的内容本身。例如，在获取图像的CCD相机，轻水平和传感器温度是主要影响因素的数量所产生的噪声的形象。图像传输过程中还损坏，由于干扰的频道用于传输。图像降噪技术，必须消除这种添加剂随机噪声，同时保留尽可能多的重要信号的功能。的主要目标，这些类型的随机噪声去除抑制噪声，同时保持原始图像的细节。统计过滤器一样平均滤波器[ 1 ] [ 2 ] ， Wiener滤波器[ 3 ]可用于消除这种噪音，但基于小波变换的去噪方法更好的结果证明不是这些过滤器。一般来说，图像去噪规定之间的妥协，减少噪音和保护重要的图像细节。为了实现良好的性能在这方面，去噪算法，以适应图像的不连续性。小波代表性，自然有利于建设这种空间自适应算法。它压缩在一个重要信息信号转换成相对较少，大量系数，代表图像细节在不同的决议尺度。在最近几年出现了相当数量的研究小波阈值和阈值选取的信号和图像去噪[ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ] ，因为小波提供了一个适当的基础分离噪音信号从图像信号。许多小波阈值技术一样VisuShrink [ 10 ] ， BayesShrink [ 11 ]已经证明，效益较好的图像去噪。在这里，我们描述一个有效的阈值去噪技术通过分析统计参数的小波系数。本文安排如下：简要回顾了离散小波变换（ DWT域）和小波滤波器银行第二节。小波阈值技术是基于解释第三节。在第四部分提出了新的阈值技术的解释。的步骤在此范围内工作的解释第五节第六节的实验结果这个拟议的工作和其他去噪技术是当前和比较。最后总结发言中给出了第七节。