shilpa suresh | Calicut University (original) (raw)

Papers by shilpa suresh

2017 Ninth International Conference on Advanced Computing (ICoAC), 2017

Researches in satellite image enhancement have been particularly confined to two major areas-cont... more Researches in satellite image enhancement have been particularly confined to two major areas-contrast enhancement and image de noising of remote sensing images. The processing of relatively dark or shadowed images necessitates the need for robust remote sensing enhancement techniques. In this paper, a robust framework for quality enhancement of multispectral remote sensing images is proposed. The quantitative results of proposed algorithm and other existing remote sensing enhancement algorithms are calculated in terms of DE, NIQMC, BIQME, PisDist and CM on different remote sensing and other image databases. Results reveal that visual enhancement of the proposed algorithm is better than other existing remote sensing enhancement algorithms. Finally, the simulation experimental results show that proposed algorithm is effective and efficient for remotes sensing as well as natural images.

International Journal of Remote Sensing, 2021

Infrared Physics & Technology, 2018

This paper proposes a robust framework for quality restoration of remotely sensed aerial images. ... more This paper proposes a robust framework for quality restoration of remotely sensed aerial images. Proposed framework works in three steps: (1) Efficient color balancing and saturation adjustment, (2) Efficient color restoration, (3) Modified contrast enhancement using particle swarm optimization (PSO). In order to show the robustness, step-wise results of proposed framework is illustrated. Several aerial images from two publically available datasets are tested to support the robustness of the proposed framework over existing image quality restoration methods. The experimental results of proposed framework and other existing quality restoration methods are compared in terms of NIQMC, BIQME, MICHELSON, DE, EME and PIXDIST along with visual experimental results. Based on experimental results conducted on several aerial images suggest that the proposed framework is outperform over existing quality restoration methods.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017

In the recent years, researchers are quite much attracted in designing two-dimensional (2-D) adap... more In the recent years, researchers are quite much attracted in designing two-dimensional (2-D) adaptive finite-impulse response (FIR) filters driven by an optimization algorithm to selfadjust the filter coefficients, with applications in different domains of research. For signal processing applications, FIR Wiener filters are commonly used for noisy signal restorations by computing the statistical estimates of the unknown signal. In this paper, a novel 2-D Cuckoo search adaptive Wiener filtering algorithm (2D-CSAWF) is proposed for the denoising of satellite images contaminated with Gaussian noise. Till date, study based on 2-D adaptive Wiener filtering driven by metaheuristic algorithms was not found in the literature to the best of our knowledge. Comparisons are made with the most studied and recent 2-D adaptive noise filtering algorithms, so as to analyze the performance and computational efficiency of the proposed algorithm. We have also included comparisons with recent adaptive metaheuristic algorithms used for satellite image denoising to ensure a fair comparison. All the algorithms are tested on the same satellite image dataset, for denoising images corrupted with three different Gaussian noise variance levels. The experimental results reveal that the proposed novel 2D-CSAWF algorithm outperforms others both quantitatively and qualitatively. Investigations were also carried out to examine the stability and computational efficiency of the proposed algorithm in denoising satellite images. Index Terms-Adaptive filter algorithm, cuckoo search (CS) algorithm, metaheuristic optimization algorithms, satellite image denoising, two-dimensional finite-impulse response (2-D FIR) Wiener filter.

Applied Soft Computing, 2017

 This paper introduces an improved variant of Darwinian PSO algorithm based on Chaotic functions... more  This paper introduces an improved variant of Darwinian PSO algorithm based on Chaotic functions  It replaces random sequences by chaotic sequences mitigating the problem of premature convergence.  Efficiency of ten defined chaotic maps are investigated and the best one was chosen.  The proposed algorithm is compared with five different chaotic variants of existing optimization algorithms.  It provides better convergence characteristics and segmentation results as compared with existing algorithms.

IEEE Transactions on Geoscience and Remote Sensing

Satellite image denoising is essential for enhancing the visual quality of images and for facilit... more Satellite image denoising is essential for enhancing the visual quality of images and for facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite-impulse response (FIR) filters attracted researchers to explore its usefulness in various domains. Furthermore, 2-D FIR Wiener filters which estimate the desired signal using its statistical parameters became a standard method employed for signal restoration applications. In this paper, we propose a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels. The ACS algorithm is proposed to optimize the Wiener weights for obtaining the best possible estimate of the desired uncorrupted image. Quantitative and qualitative comparisons are conducted with 10 recent denoising algorithms prominently used in the remote-sensing domain to substantiate the performance and computational capability of the proposed ACSWF. The tested data set included satellite images procured from various sources, such as Satpalda Geospatial Services, Satellite Imaging Corporation, and National Aeronautics and Space Administration. The stability analysis and study of convergence characteristics are also performed, which revealed the possibility of extending the ACSWF for real-time applications as well. Index Terms-2-D finite-impulse response (FIR) Wiener filter, adaptive cuckoo search (ACS) algorithm, metaheuristic optimization algorithms, satellite image denoising.

2017 Ninth International Conference on Advanced Computing (ICoAC), 2017

Researches in satellite image enhancement have been particularly confined to two major areas-cont... more Researches in satellite image enhancement have been particularly confined to two major areas-contrast enhancement and image de noising of remote sensing images. The processing of relatively dark or shadowed images necessitates the need for robust remote sensing enhancement techniques. In this paper, a robust framework for quality enhancement of multispectral remote sensing images is proposed. The quantitative results of proposed algorithm and other existing remote sensing enhancement algorithms are calculated in terms of DE, NIQMC, BIQME, PisDist and CM on different remote sensing and other image databases. Results reveal that visual enhancement of the proposed algorithm is better than other existing remote sensing enhancement algorithms. Finally, the simulation experimental results show that proposed algorithm is effective and efficient for remotes sensing as well as natural images.

International Journal of Remote Sensing, 2021

Infrared Physics & Technology, 2018

This paper proposes a robust framework for quality restoration of remotely sensed aerial images. ... more This paper proposes a robust framework for quality restoration of remotely sensed aerial images. Proposed framework works in three steps: (1) Efficient color balancing and saturation adjustment, (2) Efficient color restoration, (3) Modified contrast enhancement using particle swarm optimization (PSO). In order to show the robustness, step-wise results of proposed framework is illustrated. Several aerial images from two publically available datasets are tested to support the robustness of the proposed framework over existing image quality restoration methods. The experimental results of proposed framework and other existing quality restoration methods are compared in terms of NIQMC, BIQME, MICHELSON, DE, EME and PIXDIST along with visual experimental results. Based on experimental results conducted on several aerial images suggest that the proposed framework is outperform over existing quality restoration methods.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017

In the recent years, researchers are quite much attracted in designing two-dimensional (2-D) adap... more In the recent years, researchers are quite much attracted in designing two-dimensional (2-D) adaptive finite-impulse response (FIR) filters driven by an optimization algorithm to selfadjust the filter coefficients, with applications in different domains of research. For signal processing applications, FIR Wiener filters are commonly used for noisy signal restorations by computing the statistical estimates of the unknown signal. In this paper, a novel 2-D Cuckoo search adaptive Wiener filtering algorithm (2D-CSAWF) is proposed for the denoising of satellite images contaminated with Gaussian noise. Till date, study based on 2-D adaptive Wiener filtering driven by metaheuristic algorithms was not found in the literature to the best of our knowledge. Comparisons are made with the most studied and recent 2-D adaptive noise filtering algorithms, so as to analyze the performance and computational efficiency of the proposed algorithm. We have also included comparisons with recent adaptive metaheuristic algorithms used for satellite image denoising to ensure a fair comparison. All the algorithms are tested on the same satellite image dataset, for denoising images corrupted with three different Gaussian noise variance levels. The experimental results reveal that the proposed novel 2D-CSAWF algorithm outperforms others both quantitatively and qualitatively. Investigations were also carried out to examine the stability and computational efficiency of the proposed algorithm in denoising satellite images. Index Terms-Adaptive filter algorithm, cuckoo search (CS) algorithm, metaheuristic optimization algorithms, satellite image denoising, two-dimensional finite-impulse response (2-D FIR) Wiener filter.

Applied Soft Computing, 2017

 This paper introduces an improved variant of Darwinian PSO algorithm based on Chaotic functions... more  This paper introduces an improved variant of Darwinian PSO algorithm based on Chaotic functions  It replaces random sequences by chaotic sequences mitigating the problem of premature convergence.  Efficiency of ten defined chaotic maps are investigated and the best one was chosen.  The proposed algorithm is compared with five different chaotic variants of existing optimization algorithms.  It provides better convergence characteristics and segmentation results as compared with existing algorithms.

IEEE Transactions on Geoscience and Remote Sensing

Satellite image denoising is essential for enhancing the visual quality of images and for facilit... more Satellite image denoising is essential for enhancing the visual quality of images and for facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite-impulse response (FIR) filters attracted researchers to explore its usefulness in various domains. Furthermore, 2-D FIR Wiener filters which estimate the desired signal using its statistical parameters became a standard method employed for signal restoration applications. In this paper, we propose a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels. The ACS algorithm is proposed to optimize the Wiener weights for obtaining the best possible estimate of the desired uncorrupted image. Quantitative and qualitative comparisons are conducted with 10 recent denoising algorithms prominently used in the remote-sensing domain to substantiate the performance and computational capability of the proposed ACSWF. The tested data set included satellite images procured from various sources, such as Satpalda Geospatial Services, Satellite Imaging Corporation, and National Aeronautics and Space Administration. The stability analysis and study of convergence characteristics are also performed, which revealed the possibility of extending the ACSWF for real-time applications as well. Index Terms-2-D finite-impulse response (FIR) Wiener filter, adaptive cuckoo search (ACS) algorithm, metaheuristic optimization algorithms, satellite image denoising.