In our recent work we proposed an image denoising scheme based on reordering of the noisy image pixels to a one dimensional (1D) signal, and applying linear smoothing filters on it. This algorithm had two main limitat...
详细信息
ISBN:
(纸本)9781479903566
In our recent work we proposed an image denoising scheme based on reordering of the noisy image pixels to a one dimensional (1D) signal, and applying linear smoothing filters on it. This algorithm had two main limitations: 1) It did not take advantage of the distances between the noisy image patches, which were used in the reordering process;and 2) the smoothing filters required a separate training set to be learned from. In this work, we propose an image denoising algorithm, which applies similar permutations to the noisy image, but overcomes the above two shortcomings. We eliminate the need for learning filters by employing the nonlocal means (NL-means) algorithm. We estimate each pixel as a weighted average of noisy pixels in union of neighborhoods obtained from different global pixel permutations, where the weights are determined by distances between the patches. We show that the proposed scheme achieves results which are close to the state-of-the-art.
During acquisition, digital images are invariably degraded by a number of phenomena that limit image resolution and utility. Aliasing from undersampling, blur from optics, and sensor noise are some factors which can a...
详细信息
ISBN:
(纸本)9781467375658
During acquisition, digital images are invariably degraded by a number of phenomena that limit image resolution and utility. Aliasing from undersampling, blur from optics, and sensor noise are some factors which can affect the image resolution. Multi-frame super-resolution (SR) is a technique that takes several low-resolution (LR) frames of a particular scene and processes them together to produce one or more high-resolution (HR) images. The HR images have higher spatial frequency content, and less noise and blur, than any of the LR frames. A collaborative adaptive Wiener filter (CAWF) for multi-frame SR, proposed by the current authors, is one of the very recent effective multi-frame SR algorithms. In this paper, we modify the original CAWF SR method by employing a spatially varying signal variance estimate. Instead of using a global signal variance estimate as an external input to the original CAWF SR algorithm, we estimate the desired signal variance in each processing window and incorporate it to estimate the HR pixels. The modified CAWF SR is presented and demonstrated. In addition, performance comparisons between the original and the modified CAWF SR are conducted. The modified CAWF SR outperforms the original CAWF SR, particularly in low signal-to-noise ratio images.
Non-local means (NLM) is a powerful denoising algorithm that can protect texture effectively. However, the computational complexity of this method is so high that it is difficult to be widely applied in real-time syst...
详细信息
ISBN:
(数字)9783662477915
ISBN:
(纸本)9783662477915;9783662477908
Non-local means (NLM) is a powerful denoising algorithm that can protect texture effectively. However, the computational complexity of this method is so high that it is difficult to be widely applied in real-time systems. In this paper, we propose a fast NLM denoising algorithm which can product comparable or better result with less computation time than the traditional NLM methods. Some experimental results are provided to demonstrate the superiority of the proposed method.
We propose an image processing scheme based on reordering of its patches. For a given corrupted image, we extract all patches with overlaps, refer to these as coordinates in high-dimensional space, and order them such...
详细信息
We propose an image processing scheme based on reordering of its patches. For a given corrupted image, we extract all patches with overlaps, refer to these as coordinates in high-dimensional space, and order them such that they are chained in the "shortest possible path," essentially solving the traveling salesman problem. The obtained ordering applied to the corrupted image implies a permutation of the image pixels to what should be a regular signal. This enables us to obtain good recovery of the clean image by applying relatively simple one-dimensional smoothing operations (such as filtering or interpolation) to the reordered set of pixels. We explore the use of the proposed approach to image denoising and inpainting, and show promising results in both cases.
In our recent work we proposed an image denoising scheme based on reordering of the noisy image pixels to a one dimensional (1D) signal, and applying linear smoothing filters on it. This algorithm had two main limitat...
详细信息
ISBN:
(纸本)9781479903573
In our recent work we proposed an image denoising scheme based on reordering of the noisy image pixels to a one dimensional (1D) signal, and applying linear smoothing filters on it. This algorithm had two main limitations: 1) It did not take advantage of the distances between the noisy image patches, which were used in the reordering process;and 2) the smoothing filters required a separate training set to be learned from. In this work, we propose an image denoising algorithm, which applies similar permutations to the noisy image, but overcomes the above two shortcomings. We eliminate the need for learning filters by employing the nonlocal means (NL-means) algorithm. We estimate each pixel as a weighted average of noisy pixels in union of neighborhoods obtained from different global pixel permutations, where the weights are determined by distances between the patches. We show that the proposed scheme achieves results which are close to the state-of-the-art.
暂无评论