Block based Kalman filter algorithm for blind image separation using sparsity measure

Jyothirmayi, M; Selvi, S. Sethu

doi:10.1109/icpcsi.2017.8391923

Cited by 1 publication

(3 citation statements)

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“…In this paper, a comparison on the performances of the proposed algorithms is made with the Infomax algorithm. All simulations are carried on 4 natural images 4 [25] of dimension 256 × 512 Fig. 1(a).…”

Section: Resultsmentioning

confidence: 99%

“…Kalman filter [25] estimates the states of a linear system and minimizes the variance of the estimation error. The elementary equation for Kalman filter can be described by a time varying state space model that can be defined by the time state equations:…”

Section: Kalman Filter Algorithmmentioning

confidence: 99%

“…Here, random samples of 5000 pixels are selected from the separated image and the original source to form pixel pairs. The Mutual Information between these 5000 pixel pairs was estimated using I (1) estimator described by [25] with k = 2, (k recommended can be between 2 and 4 which is the nearest neighbor order). Table 4 shows the comparison values in terms of SNR, SSIM and MI for the algorithms discussed.…”

Section: Structural Similarity Index Metric (Ssim)mentioning

confidence: 99%

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A Comparison of Block Based Kalman Filter and H-Infinity Algorithms for Blind Image Separation

2021

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Blind image separation is a method of recovering the original images from a set of image mixtures, with no information about the source images or about the mixing process. Blind source separation problem has been used to extract sources from one-dimensional mixture signals such as speech, whereas, application of source separation for images (two-dimensional signals) has been examined to a limited extent. The independent component analysis (ICA) method assumes statistical independence of the source signals and at least one of the source could be non-gaussian. These assumptions do not hold for image mixing conditions. An alternative approach is to use Kalman filter that operates on the noisy input data recursively to produce statistically optimal estimate of the underlying sources. In this paper, a robust filter on H-infinity norm is proposed and compared with for image separation of Kalman filter. The extension of the algorithms to a simple and block based approach wherein the image mixture is converted to a sparse representation is proposed. The image mixture is subdivided into blocks of known sizes and the sparseness of each block is measured using l_0 norm and the block with maximum sparseness is used for extraction of original sources. This reduces the computational complexity that exists with the large dimensions of images. The algorithms are validated for natural image data sets and also for window reflection images taken under different lighting conditions. The analysis results suggest that the proposed methods provide significantly good quality of separation as measured by the performance metrics.

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Section: Resultsmentioning

confidence: 99%

Section: Kalman Filter Algorithmmentioning

confidence: 99%