Adaptive Super-Resolution Image Reconstruction with Lorentzian Error Norm

Mohan, S.

doi:10.17485/ijst/2017/v10i16/106780

Cited by 4 publications

(15 citation statements)

References 23 publications

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“…Where b and a represents the degraded image and original sharp image respectively, the inevitable noise in the system is represented by w as it is additive in nature it is mathematically represented through a summation. h f represent the PSF of blurring operation and * represents the convolution operation (16)(17)(18)(19) . Another alternative method of representing equation 1 will be carried out by depicting it through its spectral equivalent.…”

Section: Maximum a Posterior (Map) Estimation For The Image Deblurring Problemmentioning

confidence: 99%

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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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Section: Maximum a Posterior (Map) Estimation For The Image Deblurring Problemmentioning

confidence: 99%

“…Concerning the energy of Sobolev, it essentially measures L1 norm rather than L2 norm, the L1-norm functional is derived by removing the square as shown in equation (16). The L1-norm is the addition of absolute values of the image intensity components (34,35) .…”

Section: Total Variation Prior (Tv)mentioning

confidence: 99%

Adaptive Sparsity through Hybrid Regularization for Effective Image Deblurring

S¹,

Mallikarjunaswamy²,

Sharmila³

2021

IJST

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“…In many real-life imaging systems, there are a variety of possible factors for reducing the image resolution due to various physical constraints, inadequate photo detectors, a lower rate of spatial sampling, and an inefficient method for capturing images [8][9][10][11]. In the past years, super-resolution (SR) image reconstruction approaches have become a powerful and cost-effective solution to increase the quality of the recorded LR images, meeting the growing market demand for HR images [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…e main aim of the regularization method is to resolve the SR issue due to its ill-conditional nature. Recent works have been conducted aiming at resolving the SR issue by exploiting the regularization framework [8][9][10][11][19][20][21][22][23][24][25][26]. e idea of reconstruction of the SR image relies on the regularization process that generates the HR image through attempting to minimize the main objective function, where it incorporates both the fidelity of data and the regularization terms.…”

Section: Introductionmentioning

confidence: 99%

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Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

Khattab

Zeki

Alwan

et al. 2021

Journal of Electrical and Computer Engineering

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The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches.

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