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Journal ArticleDOI

Reconstruction of noisy and blurred images using blur kernel

01 Nov 2017-Vol. 263, Iss: 4, pp 042024
TL;DR: This work uses sparse representation to identify the blur kernel using radon transformation and Fourier for the length calculation of the image and uses Lucy Richardson algorithm which is also called NON-Blind(NBID) Algorithm for more clean and less noisy image output.
Abstract: Blur is a common in so many digital images. Blur can be caused by motion of the camera and scene object. In this work we proposed a new method for deblurring images. This work uses sparse representation to identify the blur kernel. By analyzing the image coordinates Using coarse and fine, we fetch the kernel based image coordinates and according to that observation we get the motion angle of the shaken or blurred image. Then we calculate the length of the motion kernel using radon transformation and Fourier for the length calculation of the image and we use Lucy Richardson algorithm which is also called NON-Blind(NBID) Algorithm for more clean and less noisy image output. All these operation will be performed in MATLAB IDE.
Citations
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Journal ArticleDOI
01 Oct 2020
TL;DR: In this article, an image descriptor based on Gaussian mixture model in auto-encoder (GMM-AE) was used as a primary layer in convolutional neural networks.
Abstract: In this work, deep learning for enhancing the sharpness of blurred image is investigated. Initial pre-processing is blur image kernel estimation which is critical for blind image de-blurring. In prior investigation, handcrafted blur features are optimized for certain uniform blur, which is unrealistic for blind de-convolution. To deal with this crisis, initially this work attempts to carry out kernel matrix estimation using latent semantic analysis (KME-LSA) in dermatology image. In order to enhance the image sparseness, this work modelled an image descriptor based on Gaussian mixture model in auto-encoder (GMM-AE) as a primary layer in convolutional neural networks. The functionality of the proposed GMM-AE triggers the selection of efficient features for subsequent layers in CNN. The features extracted from the integrated trained GMM-AE in CNN can fine-tune the quality of blurred image. Datasets used are melanoma-based dermascope images. Pre-processing procedures are carried out by LSA-based kernel matrix estimation. The attained sharp image outcome is given to the proposed model for effective feature extraction and to attain improved blind image. The anticipated KME-LSA and GMM-AE in CNN estimates blur parameters with high accuracy. Experiment illustrates the efficacy of proposed method and the competitive outcomes are compared with state-of-the-art datasets. Simulation was carried out in MATLAB environment; performance metrics like MSE—227.6, PSNR—33.6762, SSIM—0.9755 and VIF—0.08162 are evaluated. The results show better trade-off than the prevailing techniques.

6 citations

Journal ArticleDOI
TL;DR: Comprehensive experimental results show that the proposed dynamic structure prior (DSP) outperforms previous methods in both commonly used datasets with various noise levels and real world images, from monochrome image to color image.

3 citations

Journal ArticleDOI
TL;DR: Restoration noisy blurred images by guided filter and inverse filtering can be used for enhancing images from different types of degradation was proposed and illustrated good outcomes compared with other methods for removing noise and blur based on PSNR measure.
Abstract: The development of complex life leads into a need using images in several fields, because these images degraded during capturing the image from mobiles, cameras and persons who do not have sufficient experience in capturing images. It was important using techniques differently to improve images and human perception as image enhancement and image restoration etc. In this paper, restoration noisy blurred images by guided filter and inverse filtering can be used for enhancing images from different types of degradation was proposed. In the color images denoising process, it was very significant for improving the edge and texture information. Eliminating noise can be enhanced by the image quality. In this article, at first, The color images were taken. Then, random noise and blur were added to the images. Then, the noisy blurred image passed to the guided filtering to get on denoised image. Finally, an inverse filter applied to the blurred image by convolution an image with a mask and getting on the enhanced image. The results of this research illustrated good outcomes compared with other methods for removing noise and blur based on PSNR measure. Also, it enhanced the image and retained the edge details in the denoising process. PSNR and SSIM measures were more sensitive to Gaussian noise than blur.

2 citations


Cites methods from "Reconstruction of noisy and blurred..."

  • ...Reconstruction of noisy blurred images using blur kernel by Ellappan and Chopra [10] are used Lucy Richardson algorithm which is also named a non-blind algorithm for making the image less noisy....

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  • ...There are several methods of image restoration in the field image processing such as Median filter, guided filter, Wiener filtering, inverse filtering, Harmonic mean filter, Arithmetic mean filter, and Max filter, etc [7-10]....

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  • ...Reconstruction of noisy blurred images using blur kernel by Ellappan and Chopra [10] are used Lucy Richardson algorithm which is also named a non-blind algorithm for making the image less noisy....

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Proceedings ArticleDOI
05 Mar 2021
TL;DR: In this paper, a blind and non-blind deconvolution technique is used to restore the defocused image by removing blur and noise in the captured image using point spread function.
Abstract: Now a days the use of digital camera in day-today life is increased and along with which new challenges are rising demanding the quality improvement of captured images. Further, these quality improvement challenges and problems of captured images needs to be taken into account and required to be resolved continuously. One of the challenging tasks in the field of image processing comes out as restoration of defocused images by removing blur or noise. The blur in the defocused image generates because of different camera parameters, lens settings and observation errors while capturing the image. This paper addresses the challenge of removal of blur from the captured image using deconvolution techniques for restoration of defocused image. Use of blind and non-blind deconvolution techniques are used in this work for restoration of defocused image. The major problem is no prior knowledge of point spread function in blind deconvolution compared to non-blind deconvolution. It is also addressed in this work which is useful for further usage in computer vision and artificial intelligence. Finally, the results after implementation are presented in terms of blur type, performance parameters like signal to noise ratio (SNR), mean square error (MSE) and peak signal to noise ratio (PSNR). On the basis of these performance parameters, blind and non-blind deconvolution methods are also compared.

1 citations

Journal ArticleDOI
TL;DR: In this paper , two filters namely Wiener filter and Inverse filter are used to remove the noise and to restore the images after its quality improvement, an analysis of restored images is done on the basis of image parameters.
Abstract: Many times, the use of image processing techniques becomes a vital tool in investigation processes of security applications. This paper elaborates an attempt made in image processing domain which is needed in investigation processes of security applications to achieve deblurring of images using Wiener and Inverse filters and the comparative study on the basis of image parameters. For accurate investigation and observations, improved quality of the defocused images can be obtained using the filtering process through which blur or noise can be removed. Such blur or noise gets induced in the captured images due to shaking of security camera while continuously moving to capture the area under surveillance, improper maintenance of such camera system or unlearned lenses of the camera. The improper illumination of the area under surveillance or the fast movement of the object itself also creates sometimes a blur in the captured images. In this work, two filters namely Wiener filter and Inverse filter are used to remove the noise and to restore the images after its quality improvement. Further to understand the quality improvement of the images, an analysis of restored images is done on the basis of image parameters. Finally, the obtained results are reported in tabulated form along with the graphs of original and restored image parameters.
References
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Proceedings ArticleDOI
20 Jun 2011
TL;DR: A new type of image regularization which gives lowest cost for the true sharp image is introduced, which allows a very simple cost formulation to be used for the blind deconvolution model, obviating the need for additional methods.
Abstract: Blind image deconvolution is an ill-posed problem that requires regularization to solve. However, many common forms of image prior used in this setting have a major drawback in that the minimum of the resulting cost function does not correspond to the true sharp solution. Accordingly, a range of additional methods are needed to yield good results (Bayesian methods, adaptive cost functions, alpha-matte extraction and edge localization). In this paper we introduce a new type of image regularization which gives lowest cost for the true sharp image. This allows a very simple cost formulation to be used for the blind deconvolution model, obviating the need for additional methods. Due to its simplicity the algorithm is fast and very robust. We demonstrate our method on real images with both spatially invariant and spatially varying blur.

1,054 citations

Proceedings ArticleDOI
23 Jun 2013
TL;DR: This paper proposes a generalized and mathematically sound L0 sparse expression, together with a new effective method, for motion deblurring that does not require extra filtering during optimization and demonstrates fast energy decreasing, making a small number of iterations enough for convergence.
Abstract: We show in this paper that the success of previous maximum a posterior (MAP) based blur removal methods partly stems from their respective intermediate steps, which implicitly or explicitly create an unnatural representation containing salient image structures. We propose a generalized and mathematically sound L0 sparse expression, together with a new effective method, for motion deblurring. Our system does not require extra filtering during optimization and demonstrates fast energy decreasing, making a small number of iterations enough for convergence. It also provides a unified framework for both uniform and non-uniform motion deblurring. We extensively validate our method and show comparison with other approaches with respect to convergence speed, running time, and result quality.

1,010 citations

Proceedings ArticleDOI
01 Jun 2016
TL;DR: The first comprehensive perceptual study and analysis of single image blind deblurring using real-world blurred images and the correlation between human subjective scores and several full-reference and noreference image quality metrics is studied.
Abstract: Numerous single image blind deblurring algorithms have been proposed to restore latent sharp images under camera motion. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real blurred images. It is thus unclear how these algorithms would perform on images acquired "in the wild" and how we could gauge the progress in the field. In this paper, we aim to bridge this gap. We present the first comprehensive perceptual study and analysis of single image blind deblurring using real-world blurred images. First, we collect a dataset of real blurred images and a dataset of synthetically blurred images. Using these datasets, we conduct a large-scale user study to quantify the performance of several representative state-of-the-art blind deblurring algorithms. Second, we systematically analyze subject preferences, including the level of agreement, significance tests of score differences, and rationales for preferring one method over another. Third, we study the correlation between human subjective scores and several full-reference and noreference image quality metrics. Our evaluation and analysis indicate the performance gap between synthetically blurred images and real blurred image and sheds light on future research in single image blind deblurring.

366 citations

Journal ArticleDOI
TL;DR: This paper focuses on how to recover a motion-blurred image due to camera shake and proposes a regularization-based approach to remove motion blurring from the image by regularizing the sparsity of both the original image and the motion- Blur kernel under tight wavelet frame systems.
Abstract: How to recover a clear image from a single motion-blurred image has long been a challenging open problem in digital imaging. In this paper, we focus on how to recover a motion-blurred image due to camera shake. A regularization-based approach is proposed to remove motion blurring from the image by regularizing the sparsity of both the original image and the motion-blur kernel under tight wavelet frame systems. Furthermore, an adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problem. The experiments on both synthesized images and real images show that our algorithm can effectively remove complex motion blurring from natural images without requiring any prior information of the motion-blur kernel.

227 citations

Journal ArticleDOI
TL;DR: A novel iterative updating mechanism is proposed to refine the blur map from coarse to fine by exploiting the intrinsic relevance of similar neighbor image regions and can partially resolve the problem of differentiating an in-focus smooth region and a blurred smooth region.
Abstract: Blur exists in many digital images, it can be mainly categorized into two classes: defocus blur which is caused by optical imaging systems and motion blur which is caused by the relative motion between camera and scene objects. In this letter, we propose a simple yet effective automatic blurred image region detection method. Based on the observation that blur attenuates high-frequency components of an image, we present a blur metric based on the log averaged spectrum residual to get a coarse blur map. Then, a novel iterative updating mechanism is proposed to refine the blur map from coarse to fine by exploiting the intrinsic relevance of similar neighbor image regions. The proposed iterative updating mechanism can partially resolve the problem of differentiating an in-focus smooth region and a blurred smooth region. In addition, our iterative updating mechanism can be integrated into other image blurred region detection algorithms to refine the final results. Both quantitative and qualitative experimental results demonstrate that our proposed method is more reliable and efficient compared to various state-of-the-art methods.

74 citations