Image denoising searching similar blocks along edge directions

Liu, Jing; Liu, Ruijiao; Wang, Yinghui; Chen, Jinlei; Yang, Yajie; Ma, Douli

doi:10.1016/j.image.2017.05.001

Cited by 16 publications

(6 citation statements)

References 38 publications

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“…However, the time complexity remains unaddressed. An approach that seeks candidate matching blocks were developed in [27]. The coefficient of 3D transform was shrinking to carry out denoising.…”

Section: Related Workmentioning

confidence: 99%

Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

Natarajan¹,

Krishnan²

2022

Computer Systems Science and Engineering

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In existing methods for segmented images, either edge point extraction or preservation of edges, compromising contrast images is so sensitive to noise. The Degeneration Threshold Image Detection (DTID) framework has been proposed to improve the contrast of edge filtered images. Initially, DTID uses a Rapid Bilateral Filtering process for filtering edges of contrast images. This filter decomposes input images into base layers in the DTID framework. With minimal filtering time, Rapid Bilateral Filtering handles high dynamic contrast images for smoothening edge preservation. In the DTID framework, Rapid Bilateral Filtering with Shift-Invariant Base Pass Domain Filter is insensitive to noise. This Shift-Invariant Filtering estimates value across edges for removing outliers (i.e., noise preserving base layers of the contrast image). The intensity values are calculated in the base layer of the contrast image for accurately detecting nearby spatial locations using Shift-Invariant base Pass Domain Filter (SIDF). At last, Affine Planar Transformation is applied to detect edge filtered contrast images in the DTID framework for attaining a high quality of the image. It normalizes the translation and rotation of images. With this, Degeneration Threshold Image Detection maximizes average contrast enhancement quality and performs an experimental evaluation of factors such as detection accuracy, rate, and filtering time on contrast images. Experimental analysis shows that the DTID framework reduces the filtering time taken on contrast images by 54% and improves average contrast enhancement quality by 27% compared to GUMA, HMRF, SWT, and EHS. It provides better performance on the enhancement of average contrast enhancement quality by 28%, detection accuracy rate by 26%, and reduction in filtering time taken on contrast images by 30% compared to state-of-art methods.

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Section: Related Workmentioning

confidence: 99%

Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

Natarajan¹,

Krishnan²

2022

Computer Systems Science and Engineering

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“…Jing Liu et al [39] use 3D block-matching and 3D filtering to identify in the entire image the similar blocks. They want to identify among the edge direction all the matching blocks and to perform the denoising shrinking of the 3D transform coefficients applied on the groups.…”

Section: Introductionmentioning

confidence: 99%

Methods for Improving Image Quality for Contour and Textures Analysis Using New Wavelet Methods

2021

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The fidelity of an image subjected to digital processing, such as a contour/texture highlighting process or a noise reduction algorithm, can be evaluated based on two types of criteria: objective and subjective, sometimes the two types of criteria being considered together. Subjective criteria are the best tool for evaluating an image when the image obtained at the end of the processing is interpreted by man. The objective criteria are based on the difference, pixel by pixel, between the original and the reconstructed image and ensure a good approximation of the image quality perceived by a human observer. There is also the possibility that in evaluating the fidelity of a remade (reconstructed) image, the pixel-by-pixel differences will be weighted according to the sensitivity of the human visual system. The problem of improving medical images is particularly important in assisted diagnosis, with the aim of providing physicians with information as useful as possible in diagnosing diseases. Given that this information must be available in real time, we proposed a solution for reconstructing the contours in the images that uses a modified Wiener filter in the wavelet domain and a nonlinear cellular network and that is useful both to improve the contrast of its contours and to eliminate noise. In addition to the need to improve imaging, medical applications also need these applications to run in real time, and this need has been the basis for the design of the method described below, based on the modified Wiener filter and nonlinear cellular networks.

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“…The static wavelet transforms (SWT) use time invariance to achieve image de-noising (Wang et al, 2003). Some researchers (Zou et al, 2015; Liu et al, 2017) proposed an approach that searches for candidate matching blocks along the edges that are well-adapted to image details. All similar blocks form a 3D group.…”

Section: Introductionmentioning

confidence: 99%

Improved Wavelet Threshold for Image De-noising

et al. 2019

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With the development of communication technology and network technology, as well as the rising popularity of digital electronic products, an image has become an important carrier of access to outside information. However, images are vulnerable to noise interference during collection, transmission and storage, thereby decreasing image quality. Therefore, image noise reduction processing is necessary to obtain higher-quality images. For the characteristics of its multi-analysis, relativity removal, low entropy, and flexible bases, the wavelet transform has become a powerful tool in the field of image de-noising. The wavelet transform in application mathematics has a rapid development. De-noising methods based on wavelet transform is proposed and achieved with good results, but shortcomings still remain. Traditional threshold functions have some deficiencies in image de-noising. A hard threshold function is discontinuous, whereas a soft threshold function causes constant deviation. To address these shortcomings, a method for removing image noise is proposed in this paper. First, the method decomposes the noise image to determine the wavelet coefficients. Second, the wavelet coefficient is applied on the high-frequency part of the threshold processing by using the improved threshold function. Finally, the de-noised images are obtained to rebuild the images in accordance with the estimation in the wavelet-based conditions. Experiment results show that this method, discussed in this paper, is better than traditional hard threshold de-noising and soft threshold de-noising methods, in terms of objective effects and subjective visual effects.

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Image denoising searching similar blocks along edge directions

Cited by 16 publications

References 38 publications

Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

Methods for Improving Image Quality for Contour and Textures Analysis Using New Wavelet Methods

Improved Wavelet Threshold for Image De-noising

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