Renyi entropy and atom search sine cosine algorithm for multi focus image fusion

Singh, Vineeta; Kaushik, Vandana Dixit

doi:10.1007/s11760-020-01814-0

Cited by 10 publications

(2 citation statements)

References 24 publications

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“…Using the new approach, the maximum information about perceptual characteristics like texture and structure can be extracted using a minimum number of higher-value probability coefficients. Thus the dominant features that are robust and non-sensitive to noise represented by the probability coeffecients are given as input to the Renyi entropy model [22]. The significant achievement of this model is its excellent performance in terms of correlation with human visual perception.…”

Section: Entropymentioning

confidence: 99%

An efficient image focus measure based on perceptual features analysis

Gaidhane

2024

Preprint

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In this paper, a new approach no-reference image quality assessment (NR-IQA) focus measure based on the additivity of Renyi Entropy is proposed. In human and computer vision, image quality must be quantified with human perception. Therefore, perceptual features such as image texture and structure are analyzed. It allows us to achieve a better correlation with the subjective quality assessment. The probability coefficients of images are obtained to extract the human visual system (HVS) features, and significant image details are evaluated. The randomness in the information of the image is observed by considering the additivity of Renyi Entropy. The majority of objective IQA algorithms evaluate the image quality by comparing the original image with the distorted. The presented approach is tested on artificial images by introducing a certain amount of blur without noise and in the presence of noise. The effectuality and performance of the presented method on real-time images show consistent responses under blurred and noisy conditions. Moreover, the proposed method is evaluated with three publicly available data sets such as LIVE, TID2013, and CSIQ. The presented method is compared with various existing techniques in the literature, and it is evident from the experiments that the method has better monotonicity and improved focus measures. The proposed approach achieved better performance metrics such as SROCC, KRCC, and PLCC. The computation time and complexity of the presented approach are reduced due to the logarithmic function.

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Section: Entropymentioning

confidence: 99%

An efficient image focus measure based on perceptual features analysis

Gaidhane

2024

Preprint

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“…Image fusion at the pixel level can be divided into fusion in the spatial domain and fusion in the transform domain, and the method proposed in this paper belongs to transform domain fusion in pixel-level fusion. In transform domain fusion, common methods that have been proposed in the past are Laplacian pyramid decomposition [5], Curvelet transform [6], Contourlet transform [7], Nonsubsampled Contourlet transform [8], discrete wavelet transform (DWT) [9] and other algorithms. Among them, the most commonly used is DWT algorithm, which has played an important role in image fusion since Mallat (1999) [10] presented a rapid algorithm for decomposing and reconstructing the wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

A novel method of greyscale image fusion based on discrete fractional wavelet transform combined with improved chaotic genetic algorithm

Wang,

Xie,

Yang

2023

Preprint

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Greyscale image fusion plays a crucial role in computer vision and image processing. This paper proposes a novel algorithm for greyscale image fusion that enhances clarity and information retention of the resulting image. Firstly, the source image is decomposed using discrete fractional wavelet transform (DFRWT) to acquire the low and high frequency components. Secondly, considering the different characteristics of low and high frequencies, the spatial frequency technique is chosen to address the coefficients in the low frequency domain. In the high-frequency part, an improved chaotic genetic algorithm (ICGA) is used to optimize the high-frequency fusion coefficients, which can adaptively adjust the weighting to avoid the blurring and distortion problems that are likely to occur when the DFRWT processes the details, as a consequence, the features of the original image are better retained, making the fused image more natural and the details clearer. Finally, the inverse DFRWT is performed to get the fusion results. Simulation experiments prove the effectiveness of the new algorithm.

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A Typical Hybrid Optimization-Based Image Quality Enhancement Technique

Singh

Kaushik

2022

Algorithms for Intelligent Systems

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Renyi entropy and atom search sine cosine algorithm for multi focus image fusion

Cited by 10 publications

References 24 publications

An efficient image focus measure based on perceptual features analysis

An efficient image focus measure based on perceptual features analysis

A novel method of greyscale image fusion based on discrete fractional wavelet transform combined with improved chaotic genetic algorithm

A Typical Hybrid Optimization-Based Image Quality Enhancement Technique

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