Blind Image Seperation Using Forward Difference Method (FDM)

Jyothirmayi, M

doi:10.5121/sipij.2011.2410

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The performance of separated images is evaluated by using peak signal to noise ratio (PNSR) [19], which is defined as follows, and shown in Tables 1-4.…”

Section: Simulationmentioning

confidence: 99%

Blind Image Separation Using the JADE Method

Saeed¹,

Zohaib²

2022

The 1st International Conference on Computational Engineering and Intelligent Systems

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In this paper, we characterized and reviewed the emergence of fundamental and extended losses that limit the efficiency of a photovoltaic (PV) system. Although there is an upper theoretical bound to the power conversion efficiency of solar cells, i.e., the Shockley Queisser limit, in a practical environment, the consideration of inevitable losses in a whole PV system is imperative to optimally harvest solar energy. In this regard, this study quantifies the losses from a PV cell level to the whole PV system. It was perceived that reported losses on the PV cell level included the low energy bandgap, thermalization, recombination (surface and bulk recombination), optical absorption, space charge region, finite thickness, and metal contact loss, and it was determined that cutting techniques mainly constrained the power conversion efficiency of the solar cell. Furthermore, the detailed PV array losses were classified as mismatch power losses, dust accumulation losses, temperature effects, material quality losses, and ohmic wiring losses. The unavoidable system losses were quantified as inverter losses, maximum power point tracking losses, battery losses, and polarization losses. The study also provides insights into potential approaches to combat these losses and can become a useful guide to better visualize the overall phenomenology of a PV System.

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“…The performance of separated images is evaluated by using peak signal to noise ratio (PNSR) [19], which is defined as follows, and shown in Tables 1-4.…”

Section: Simulationmentioning

confidence: 99%

Blind Image Separation Using the JADE Method

Saeed¹,

Zohaib²

2022

The 1st International Conference on Computational Engineering and Intelligent Systems

View full text Add to dashboard Cite

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“…The matrix identification algorithm is much simpler when the sources are locally sparse. Sparse representation of image matrix can be performed using clustering algorithms like gradient ascent learning (GAL), Laplacian prior (LP), Wavelet (WT) [10][11][12], Finite difference (FD) approach [13] and Hessian approach (HA) [9], etc. Finite difference approach and Hessian approach are discussed in detail in [13] and [9].…”

Section: Introductionmentioning

confidence: 99%

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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Blind Image Seperation Using Forward Difference Method (FDM)

Cited by 2 publications

References 11 publications

Blind Image Separation Using the JADE Method

Blind Image Separation Using the JADE Method

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

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