26,595 research outputs found
Designing A Composite Dictionary Adaptively From Joint Examples
We study the complementary behaviors of external and internal examples in
image restoration, and are motivated to formulate a composite dictionary design
framework. The composite dictionary consists of the global part learned from
external examples, and the sample-specific part learned from internal examples.
The dictionary atoms in both parts are further adaptively weighted to emphasize
their model statistics. Experiments demonstrate that the joint utilization of
external and internal examples leads to substantial improvements, with
successful applications in image denoising and super resolution
Image Denoising via Style Disentanglement
Image denoising is a fundamental task in low-level computer vision. While
recent deep learning-based image denoising methods have achieved impressive
performance, they are black-box models and the underlying denoising principle
remains unclear. In this paper, we propose a novel approach to image denoising
that offers both clear denoising mechanism and good performance. We view noise
as a type of image style and remove it by incorporating noise-free styles
derived from clean images. To achieve this, we design novel losses and network
modules to extract noisy styles from noisy images and noise-free styles from
clean images. The noise-free style induces low-response activations for noise
features and high-response activations for content features in the feature
space. This leads to the separation of clean contents from noise, effectively
denoising the image. Unlike disentanglement-based image editing tasks that edit
semantic-level attributes using styles, our main contribution lies in editing
pixel-level attributes through global noise-free styles. We conduct extensive
experiments on synthetic noise removal and real-world image denoising datasets
(SIDD and DND), demonstrating the effectiveness of our method in terms of both
PSNR and SSIM metrics. Moreover, we experimentally validate that our method
offers good interpretability
Improved Non-Local Means Algorithm Based on Dimensionality Reduction
Non-Local Means is an image denoising algorithm based on patch similarity. It compares a reference patch with the neighboring patches to find similar patches. Such similar patches participate in the weighted averaging process. Most of the computational time for Non-Local Means is consumed to measure patch similarity. In this thesis, we have proposed an improvement where the image patches are projected into a global feature space. Then we have performed a statistical t-test to reduce the dimensionality of this feature space. Denoising is achieved based on this reduced feature space and the proposed modification exploits an improvement in terms of denoising performance and computational time
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