Improved Procedure for Multi-Focus Images Using Image Fusion with qshiftN DTCWT and MPCA in Laplacian Pyramid Domain

Mohan, Chinnem Rama; Chouhan, Kuldeep; Rout, Ranjeet Kumar; Sahoo, Kshira Sagar; Jhanjhi, N. Z.; Ibrahim, Ashraf Osman; Abdelmaboud, Abdelzahir

doi:10.3390/app12199495

Cited by 10 publications

(7 citation statements)

References 54 publications

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“…As was the case with the experiment carried out by Balasubramanian et al [8], the effectiveness of the proposed denoising method is evaluated on four standard test images: Lena, the boat, the house, and the cameraman corrupted by AWGN with zero mean and standard deviation σ=10 to 90 with increment 10. The proposed denoising method has been implemented by writing Python code.…”

Section: Resultsmentioning

confidence: 99%

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Image Denoising using Wavelet Cycle Spinning and Non-local Means Filter

Karyono¹,

Asmai²

2023

IJACSA

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Removing as much noise as possible in an image while preserving its fine details is a complex and challenging task. We propose a wavelet-based and non-local means (NLM) denoising method to overcome the problem. Two well-known wavelets: dual-tree complex wavelet transform (DT-CWT) and discrete wavelet transform (DWT), have been used to change the noise image into several wavelet coefficients sequentially. NLM filtering and universal hard thresholding with cycle spinning have been used for thresholding on its approximation and detail coefficients, respectively. The inverse two-dimensional DWT was applied to the modified wavelet coefficients to obtain the denoised image. We conducted experiments with twelve test images on the set12 data set, adding the additive Gaussian white noise with variances of 10 to 90 in increments of 10. Three evaluation metrics, such as peak signal noise to rate (PSNR), structural similarity index metric (SSIM), and mean square error (MSE), have been used to evaluate the effectiveness of the proposed denoising method. From these measurement results, the proposed denoising method outperforms DT-CWT, DWT, and NLM almost in all noise levels except for the noise level of 10. At that noise level, the proposed denoising method is lower than NLM but better than DT-CWT and DWT.

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Section: Resultsmentioning

confidence: 99%

“…In most of them, discrete wavelet transform (DWT) was widely used, but it has three other main issues. These issues are lack of poor directionality, shiftinvariant, and aliasing [8]. Conversely, the non-local means filter is highly effective in retaining the proper morphology of the signal at low-frequency.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising using Wavelet Cycle Spinning and Non-local Means Filter

Karyono¹,

Asmai²

2023

IJACSA

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“…The quality of edge detection can be expressed in terms of the Pratt quality factor, which focuses on the three errors of missing valid edges, edge localization errors and misjudging noise as edges [30][31], and takes values from 0 to 1. It is calculated as follows:…”

Section: Analysis and Discussionmentioning

confidence: 99%

Research on Image Denoising in edge detection Based on Wavelet Transform

Ning¹,

Han²,

Zhu³

et al. 2022

Preprint

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In the process of image feature extraction, noise in the image will greatly affect the accuracy of edge detection. In this paper, the image is filtered to remove noise before edge detection by using the algorithm of wavelet transformation. Different wavelet functions are used to decompose image. Based on the experimental results, the best denoising wavelet function is selected. Canny algorithm is used to detect the edge of the denoised image, and the result of edge detection is evaluated according to the Pratt quality factor. It is proved that wavelet transform can improve the edge detection results.

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“…The dual-tree complex wavelet transform (DTCWT) overcomes the shift variance and poor directional selectivity difficulties seen in the frequently used discrete wavelet transform (DWT) in two and higher dimensions [18]. It has been suggested for use in applications including texture classification and content-based picture retrieval, among others.…”

Section: Introductionmentioning

confidence: 99%

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

Keerthi Kiran,

Dash,

Parida

2024

Eng. Res. Express

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Traffic congestion is prevalent in many major and medium-sized cities throughout different countries in contemporary society. In traffic images, various multi-sized vehicles are tightly clustered together and obstructed from one another. Identifying vehicles in such instances is crucial for urban traffic surveillance, safety monitoring, and legal concerns but it also presents major challenges. The remarkable detection accuracy and efficiency of deep learning-based systems have led to their recent and extensive use in vehicle identification. There are significant advanced YOLO models with different backbone architectures and frameworks developed for vehicle detection. Yet, the performance of YOLO variants is facing the challenges of handling false detection against occluded and densely sophisticated scenarios. The proposed model is developed to address such types of limitations, for example; dynamic illumination, noisy images, and scale sensitivity to improve the vehicle detection rate in different traffic scenarios and varying weather conditions. The proposed study employs an improved YOLOv4 to identify moving vehicles in different lighting conditions including daylight, cloudy, rainy, and night. For hybridization, three techniques are utilized such as the Multiscale Retinex, Dual tree complex wavelet transform (DTCWT), and Pulse Coupled Neural Networks (PCNN). The DTCWT is employed for multiscale decomposition and to denoise the complex high frequency subband information, then the denoised subbands are reconstructed into a denoised image. The Multiscale retinex is utilized to reduce the halo artifacts on high-contrast edges and maintain the balance with dynamic range compression and color reproduction. The synchronizing pulse burst property of PCNN is used to detect the isolated noisy pixels and modify the detected noisy pixels. From the results it is worth noting that the developed model surpasses state-of-the-art methods in sunny, night, cloudy, and rainy modes. The proposed method using the DTCWT technique can detect the vehicles with mAP of 91.095% and 35FPS.

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Improved Procedure for Multi-Focus Images Using Image Fusion with qshiftN DTCWT and MPCA in Laplacian Pyramid Domain

Cited by 10 publications

References 54 publications

Image Denoising using Wavelet Cycle Spinning and Non-local Means Filter

Image Denoising using Wavelet Cycle Spinning and Non-local Means Filter

Research on Image Denoising in edge detection Based on Wavelet Transform

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

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