Effect of Laplacian of Gaussian Filter on Watermark Retrieval in Spatial domain Watermarking

Saffari, Vahid; Ghazimoradi, Amirsoheil; Alirezanejad, Mehdi

doi:10.17485/ijst/2015/v8i1/71226

Cited by 4 publications

(3 citation statements)

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“…LoG edge detection method operated image is shown in Figure 4. Laplacian filter is susceptible to noise [11]. To decrease the noise effect, Gaussian filter could be used.…”

Section: Log ( Laplacian Of Gausian) Edge Detection Techniquementioning

confidence: 99%

“…Thus, in Laplacian of Gaussian (LOG) operator, at first the Gaussian smoothing is applied, then the Laplacian operation is performed. The high frequency noise components could decrease by this combination [11]. The sensibility to noise is decrease by using the combination of Gaussian function and Laplacian mask.…”

Section: Log ( Laplacian Of Gausian) Edge Detection Techniquementioning

confidence: 99%

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Combination of Thresholding and Log Edge Detection Technique in Thermal Image Analysis

Pithadiya¹

2017

IJRASET

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Accuracy itself a complete statement to describe a final destination. Now days, we have so many image processing tools available. By using that, it is very easy to extract the information or details of background of an Image taken from thermal imaging camera. Without any special function or a tool it is a very cumbersome task to be carried out for any researcher. Normally, thresholding technique is applied to separate out different objects from the background present in an image. To get an accurate result and enhancement in analysis and measurement of any object of thermal image, it is required to apply essential steps in an algorithm used in addition with thresholding. In this paper, thermal image of an electrical system is used. To protect the circuit, it is necessary to distinguish area of an acquired thermal image by high heated area and normally heated area. Now to protect circuit, it is necessary to measure high heated area accurately where normally heated area is ignored. In this paper I have used LoG (Laplacian of Gaussian), an optimal edge detection technique in addition with thresholding, and achieved high accuracy in a result identified. The result or measured area is in terms of pixel for simplicity.

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“…LoG edge detection method operated image is shown in Figure 4. Laplacian filter is susceptible to noise [11]. To decrease the noise effect, Gaussian filter could be used.…”

Section: Log ( Laplacian Of Gausian) Edge Detection Techniquementioning

confidence: 99%

Section: Log ( Laplacian Of Gausian) Edge Detection Techniquementioning

confidence: 99%

Combination of Thresholding and Log Edge Detection Technique in Thermal Image Analysis

Pithadiya¹

2017

IJRASET

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“…Various such techniques have been proposed, for example in (2) a unique filter is applied on the deblurred images to recover the parts which are in focus and another image is used to recover the portions which are not in focus. Vahid Saffari et al (3) introduces the novel nonparametric regression technique for the deblurring of blurred and noisy images which depends on the idea of the LPA (Local Polynomial Approximation) of an image with the consideration of Intersecting Confidence Intervals (ICI) which is adapted to characterize a versatile changing parameter i.e., sizes of window in an LPA assessor. Mean Curvature (MC) Regularization is used to enhance the quality of deblurred images.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sparsity through Hybrid Regularization for Effective Image Deblurring

S¹,

Mallikarjunaswamy²,

Sharmila³

2021

IJST

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Objectives: To develop image deblurring algorithm to effectively recover the original image from the captured blurry and noisy image. Methods: A novel image deblurring algorithm using adaptive priors is proposed. The adaptive priors vary the sparsity induced based on factors such as the type of blur affecting the image, image region, statistical parameters regarding the blur kernel and/or statistical predictions on the blur kernel. The proposed algorithm using adaptive priors improves the quality of the deblurred result, when compared with various recent image deblurring algorithms. Finding: the work contains a case study with regard to certain standard parameters. It is observed that proposed method is better in terms of frequency response, Peak signal-tonoise ratio (PSNR) and Structural Similarity Index (SSIM) values in comparison with other priors. Novelty: the proposed priors lead to the most effective results for image deblurring using the Bayesian framework. The proposed method improves the performance by 30% in PSNR and 45% in SSIM values in dB with uniform kernel size 12 * 12 and improves performance by 30 % in PSNR and 32% SSIM with a standard deviation of 3.5. The proposed method enhances the frequency response of the real-time image restoration process. Applications: Some important applications include the restoration of medical images such as MRI images, CT images where the intensity of these radiations is maintained to avoid damage to human organs. Remote sensing images captured through drones at a specified time cannot be retaken and hence restoring such images is very important. Similarly restoring images from CCTV footage, astronomical images.

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