Topic
Impulse noise
About: Impulse noise is a research topic. Over the lifetime, 4816 publications have been published within this topic receiving 63970 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: The extensive simulations show that GLSSTV is effective in removing mixed noise both quantitatively and qualitatively and it outperforms the state-of-the-art low-rank and TV-based methods.
Abstract: Hyperspectral images (HSIs) are frequently corrupted by various types of noise, such as Gaussian noise, impulse noise, stripes, and deadlines due to the atmospheric conditions or imperfect hyperspectral imaging sensors. These types of noise, which are also called mixed noise, severely degrade the HSI and limit the performance of post-processing operations, such as classification, unmixing, target recognition, and so on. The patch-based low-rank and sparse based approaches have shown their ability to remove these types of noise to some extent. In order to remove the mixed noise further, total variation (TV)-based methods are utilized to denoise HSI. In this paper, we propose a group low-rank and spatial-spectral TV (GLSSTV) to denoise HSI. Here, the advantage is twofold. First, group low-rank exploits the local similarity inside patches and non-local similarity between patches which brings extra structural information. Second, SSTV helps in removing Gaussian and sparse noise using the spatial and spectral smoothness of HSI. The extensive simulations show that GLSSTV is effective in removing mixed noise both quantitatively and qualitatively and it outperforms the state-of-the-art low-rank and TV-based methods.
17 citations
TL;DR: Experimental results show that the proposed adaptive algorithm to reduce the impulse noise for high noise images is capable to provide a better picture quality than the median filters and which is faster than alternatives.
17 citations
Journal Article•
TL;DR: The experimental results show the better performance of the proposed method than a number of existing schemes for color images corrupted by high percentage of fixed-valued and random-valued impulse noises.
Abstract: This paper presents a vector median filter that incorporates mechanism for the detection of impulses from color images. The vector pixels in a specified window is ranked on the basis of sum of the distances to other vector pixels in the window. The center vector pixel is declared as corrupted if its rank is bigger than a predefined rank and its distance from a nearby healthy vector pixel is bigger than a predefined threshold. The corrupted vector pixel is replaced by the vector median. The experimental results show the better performance of the proposed method than a number of existing schemes for color images corrupted by high percentage of impulse noise.
17 citations
TL;DR: This paper proposes a spatial non-local and local rank-constrained low-rank regularized Plug-and-Play model for mixed noise removal in HSIs, and develops an efficient algorithm for solving the proposed NLRPnP model by using the alternating direction method of multipliers method.
Abstract: Hyperspectral images (HSIs) are usually corrupted by various noises during the image acquisition process, e.g., Gaussian noise, impulse noise, stripes, deadlines and many others. Such complex noise severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSI applications. In this paper, a spatial non-local and local rank-constrained low-rank regularized Plug-and-Play (NLRPnP) model is presented for mixed noise removal in HSIs. Specifically, we first divide HSIs into local overlapping patches. Local rank-constrained low-rank matrix recovery is adopted to effectively separate the low-rank clean HSI patches from the sparse noise and a part of Gaussian noise, and to significantly preserve local structure and detail information in HSIs. Then the spatial non-local based denoiser is introduced to promote the non-local self-similarity and obviously depress the Gaussian noise. Without increasing the difficulty of solving optimization problems, we combine the local and non-local based methods into the Plug-and-Play framework, and develop an efficient algorithm for solving the proposed NLRPnP model by using the alternating direction method of multipliers method. Finally, several experiments are conducted in both simulated and real data conditions to illustrate the better performance of the proposed NLRPnP model than the existing state-of-the-art denoising models.
17 citations
Patent•
18 Oct 2013
TL;DR: In this paper, a dual sensor receiver is used to deal with the impulse noise effectively, and a power line sensor can also act as a sensor to cancel the noise in a DSL receiver.
Abstract: The present invention generally relates to an impulse noise canceller for DSL systems According to certain aspects, embodiments of the invention provide a dual sensor receiver to deal with the impulse noise effectively The second sensor can be incorporated by either a common mode or unused differential port Alternatively a power line sensor can also act as a sensor According to certain additional aspects, embodiments of the invention provide various alternative implementations of an impulse noise canceller within a DSL receiver, According to still further aspects, embodiments of the invention provide methods for selectively training an impulse noise canceller in the various implementations,
17 citations