- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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High-Dimensional Mixture Models for Unsupervised Image Denoising (HDMI)
摘要： This work addresses the problem of patch-based image denoising through the unsupervised learning of a probabilistic high-dimensional mixture model on the noisy patches. The model, called HDMI, proposes a full modeling of the process that is supposed to have generated the noisy patches. To overcome the potential estimation problems due to the high dimension of the patches, the HDMI model adopts a parsimonious modeling which assumes that the data live in group-specific subspaces of low dimensionalities. This parsimonious modeling allows us in turn to get a numerically stable computation of the conditional expectation of the image which is applied for denoising. The use of such a model also permits us to rely on model selection tools, such as BIC, to automatically determine the intrinsic dimensions of the subspaces and the variance of the noise. This yields a denoising algorithm that can be used both when the noise level is known and is unknown.
关键词： image denoising,parsimonious mixture model,model selection,high-dimensional clustering,intrinsic dimension estimation,patch-based representation
更新于2025-09-04 15:30:14
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A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure
摘要： Multi-exposure image fusion methods are often applied to the fusion of low-dynamic images that are taken from the same scene at different exposure levels. The fused images not only contain more color and detailed information, but also demonstrate the same real visual effects as the observation by the human eye. This paper proposes a novel multi-exposure image fusion (MEF) method based on adaptive patch structure. The proposed algorithm combines image cartoon-texture decomposition, image patch structure decomposition, and the structural similarity index to improve the local contrast of the image. Moreover, the proposed method can capture more detailed information of source images and produce more vivid high-dynamic-range (HDR) images. Speci?cally, image texture entropy values are used to evaluate image local information for adaptive selection of image patch size. The intermediate fused image is obtained by the proposed structure patch decomposition algorithm. Finally, the intermediate fused image is optimized by using the structural similarity index to obtain the ?nal fused HDR image. The results of comparative experiments show that the proposed method can obtain high-quality HDR images with better visual effects and more detailed information.
关键词： texture information entropy,adaptive selection,multi-exposure image fusion,patch structure decomposition
更新于2025-09-04 15:30:14