论文标题
使用非本地过滤的时空光谱图像重建
Spatio-spectral Image Reconstruction Using Non-local Filtering
论文作者
论文摘要
在许多图像处理任务中，仅在某些频道中缺少或丢失像素或像素块。例如，在RGB图像的有缺陷的传输过程中，可能会丢失一个或多个颜色通道中的一个或多个块。在图像处理和传输中，几乎所有现代应用都使用至少三个颜色通道，其中一些应用程序采用了更多的频段，例如在光谱的红外和紫外线区域中。通常，在颜色通道子集中只有一些像素和块会被扭曲。因此，其他通道可用于重建缺失的像素，这称为时空光谱重建。当前的最新方法纯粹依赖于当地社区，该邻里适合同质区域。但是，在高频区域（例如边缘或纹理）中，这些方法无法正确对颜色带之间的关系进行建模。因此，本文介绍了用于构建线性回归模型的非本地过滤，该模型描述了带间关系，并用于重建缺失的像素。我们的新方法能够平均将PSNR增加2 dB，并在高频区域的视觉上产生更具吸引力的图像。
In many image processing tasks it occurs that pixels or blocks of pixels are missing or lost in only some channels. For example during defective transmissions of RGB images, it may happen that one or more blocks in one color channel are lost. Nearly all modern applications in image processing and transmission use at least three color channels, some of the applications employ even more bands, for example in the infrared and ultraviolet area of the light spectrum. Typically, only some pixels and blocks in a subset of color channels are distorted. Thus, other channels can be used to reconstruct the missing pixels, which is called spatio-spectral reconstruction. Current state-of-the-art methods purely rely on the local neighborhood, which works well for homogeneous regions. However, in high-frequency regions like edges or textures, these methods fail to properly model the relationship between color bands. Hence, this paper introduces non-local filtering for building a linear regression model that describes the inter-band relationship and is used to reconstruct the missing pixels. Our novel method is able to increase the PSNR on average by 2 dB and yields visually much more appealing images in high-frequency regions.
