A robust iterative algorithm for image restoration

Liu, Yuewei; Lu, Weiping

doi:10.1186/s13640-017-0201-6

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…The goal of the BID is to recover a blur kernel and a sharp latent from a blurred input, which is a typical ill-posed problem. Solution for the ill-posed problem can be categorized into direct methods via some effective regularization [10], [11], [12], [17], [20], [21] and iterative methods via some updating and stopping rules [15], [22]. Recently, learning-based BID approaches have drawn much attention due to the satisfactorily restored qualities [16].…”

Section: Retalted Workmentioning

confidence: 99%

Gradient-wise search strategy for blind image deblurring

Wang

Liu

2022

MATEC Web Conf.

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Blind image deblurring is a long-standing challenging problem to improve the sharpness of an image as a prerequisite step. Many iterative methods are widely used for the deblurring image, but care must be taken to ensure that the methods have fast convergence and accuracy solutions. To address these problems, we propose a gradient-wise step size search strategy for iterative methods to achieve robustness and accelerate the deblurring process. We further modify the conjugate gradient method with the proposed strategy to solve the bling image deblurring problem. The gradient-wise step size aims to update gradient for each pixel individually, instead of updating step size by the fixed factor. The modified conjugate gradient method improves the convergence performance computation speed with a gradient-wise step size. Experimental results show that our method effectively estimates the sharp image for both motion blur images and defocused images. The results of synthetic datasets and natural images are better than what is achieved with other state-of-the-art blind image deblurring methods.

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Section: Retalted Workmentioning

confidence: 99%

Gradient-wise search strategy for blind image deblurring

Wang

Liu

2022

MATEC Web Conf.

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“…Typically, the images are often corrupted by several noises, haze, fog, and blur due to an error generated in noisy sensors or communication channels. In recent years, researchers have developed several methods, such as the cascaded model of Gaussian conditional random fields [28], splitting method for structured total least squares [13], Bayesian model [19,30], the combination of iterative VanCittert algorithm with noise reduction modeling [21], K-means singular value decomposition [39], joint log likelihood function [45], and filters [42], for restoring corrupted images. In addition, various kinds of neural networks and classifiers have been utilized in the IR process.…”

Section: Survey Of Related Workmentioning

confidence: 99%

Linear Regression Supporting Vector Machine and Hybrid LOG Filter-Based Image Restoration

Basha

Venkateswarlu

2019

Journal of Intelligent Systems

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Abstract The image restoration (IR) technique is a part of image processing to improve the quality of an image that is affected by noise and blur. Thus, IR is required to attain a better quality of image. In this paper, IR is performed using linear regression-based support vector machine (LR-SVM). This LR-SVM has two steps: training and testing. The training and testing stages have a distinct windowing process for extracting blocks from the images. The LR-SVM is trained through a block-by-block training sequence. The extracted block-by-block values of images are used to enhance the classification process of IR. In training, the imperfections on the image are easily identified by setting the target vectors as the original images. Then, the noisy image is given at LR-SVM testing, based on the original image restored from the dictionary. Finally, the image block from the testing stage is enhanced using the hybrid Laplacian of Gaussian (HLOG) filter. The denoising of the HLOG filter provides enhanced results by using block-by-block values. This proposed approach is named as LR-SVM-HLOG. A dataset used in this LR-SVM-HLOG method is the Berkeley Segmentation Database. The performance of LR-SVM-HLOG was analyzed as peak signal-to-noise ratio (PSNR) and structural similarity index. The PSNR values of the house and pepper image (color image) are 40.82 and 36.56 dB, respectively, which are higher compared to the inter- and intra-block sparse estimation method and block matching and three-dimensional filtering for color images at 20% noise.

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“…One of the conventional approaches to produce highquality images is to perform histogram equalization on pixel intensities [21]. The use of iterative methods, gamma correction, and homomorphic filtering has also been an attractive approach [22][23][24]. When the captured image has a poor quality, more sophisticated techniques need to be employed.…”

Section: Introductionmentioning

confidence: 99%

Restoration of online video ferrography images for out-of-focus degradations

Yan

et al. 2018

J Image Video Proc.

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Ferrography is a technology that can be applied in inspecting features of wear particles in machines and inferring their health status. With the development of online ferrography, which employs image processing to captured wear particle images, the inspection process has become automatic. However, it is found that images captured often contain out-of-focus degradations and low brightness. A restoration framework is here proposed to mitigate this problem. The main idea is to extract object edges, magnify with a non-linear gain factor, then combine with the input image to produce an enhanced image to facilitate further analysis. Parameters adopted in the process are optimized using a metaheuristic search where the image information content and brightness are maximized. Experimental results, obtained from processing real-world wear particle images in lubricant circuits, have shown qualitative and quantitative improvements over the input images.

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A robust iterative algorithm for image restoration

Cited by 8 publications

References 23 publications

Gradient-wise search strategy for blind image deblurring

Gradient-wise search strategy for blind image deblurring

Linear Regression Supporting Vector Machine and Hybrid LOG Filter-Based Image Restoration

Restoration of online video ferrography images for out-of-focus degradations

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