scholarly journals Joint Bilateral Filter for Signal Recovery from Phase Preserved Curvelet Coefficients for Image Denoising

2021 ◽  
pp. 2150049
Author(s):  
Susant Kumar Panigrahi ◽  
Supratim Gupta
Keyword(s):  
Image Denoising ◽  
Bilateral Filter ◽  
Noise Sensitivity ◽  
Signal Recovery ◽  
Bilateral Filtering ◽  
Hard Thresholding ◽  
Guided Image Filter ◽  
Ringing Artifacts ◽  
Denoised Image ◽  
Lower Noise

Thresholding of Curvelet Coefficients, for image denoising, drains out subtle signal component in noise subspace. In effect, it also produces ringing artifacts near edges. We found that the noise sensitivity of Curvelet phases — in contrast to their magnitude — reduces with higher noise level. Thus, we preserved the phase of the coefficients below threshold at coarser scale and estimated the corresponding magnitude by Joint Bilateral Filtering (JBF) technique. In contrast to the traditional hard thresholding, the coefficients in the finest scale is estimated using Bilateral Filtering (BF). The proposed filtering approach in the finest scale exhibits better connectedness among the edges, while removing the granular artifacts in the denoised image due to hard thresholding. Finally, the use of Guided Image Filter (GIF) on the Curvelet-based reconstructed image (initial denoised image in spatial domain) ensures the preservation of small image information with sharper edges and textures detail in the final denoised image. The lower noise sensitivity of Curvelet phase at higher noise strength accelerates the performance of proposed method over several state-of-the-art techniques and provides comparable outcome at lower noise levels.

scholarly journals An Efficient Image Denoising Scheme for Higher Noise Levels Using Spatial Domain Filters

2018 ◽  
Vol 11 (2) ◽  
pp. 625-634 ◽  
Author(s):  
Anchal Anchal ◽  
Sumit Budhiraja ◽  
Bhawna Goyal ◽  
Ayush Dogra ◽  
Sunil Agrawal
Keyword(s):  
Image Denoising ◽  
Analog Circuits ◽  
Anisotropic Diffusion ◽  
Additive White Gaussian Noise ◽  
Bilateral Filter ◽  
Noise Levels ◽  
Significant Information ◽  
Edge Information ◽  
Diffusion Filtering ◽  
Lower Noise

Image denoising is one of the fundamental image processing problem. Images are corrupted with additive white Gaussian noise during image acquisition and transmission over analog circuits. In medical images the prevalence of noise can be perceived as tumours or artefacts and can lead to first diagnosis. Similarly in satellite images the visibility of images is significantly degraded due to noise, hence the image denoising is of vital importance. There are many denoising mechanisms given in literature are able to work well on lower noise levels but their performance degrades with increasing noise levels. If higher amount of filtering is applied it leads to degradation or removal of edges from the image and hence significant information. In this paper, we proposed an algorithm in which we are able to address the problem of image denoising at higher noise levels while preserving the edge information. The standard bilateral filter does not provides good results at higher noise levels. Hence we proposed to combine robust bilateral filtering with anisotropic diffusion filtering as the anisotropic diffusion perform the smoothing of homogenous regions without blurring the edges. Experimental results show that the proposed method works better for higher Nosie levels in terms of PSNR values and Visual quality.

Performance Assessment of Edge Preserving Filters

2017 ◽  
Vol 8 (2) ◽  
pp. 1-29
Author(s):  
Kamireddy Rasool Reddy ◽  
Madhava Rao Ch ◽  
Nagi Reddy Kalikiri
Keyword(s):  
Bilateral Filter ◽  
Wavelet Thresholding ◽  
Bilateral Filtering ◽  
Noisy Image ◽  
Edge Preserving ◽  
Local Means ◽  
Non Local ◽  
Assess Quality ◽  
Filter Algorithms ◽  
Denoised Image

Denoising is one of the important aspects in image processing applications. Denoising is the process of eliminating the noise from the noisy image. In most cases, noise accumulates at the edges. So that prevention of noise at edges is one of the most prominent problem. There are numerous edge preserving approaches available to reduce the noise at edges in that Gaussian filter, bilateral filter and non-local means filtering are the popular approaches but in these approaches denoised image suffer from blurring. To overcome these problems, in this article a Gaussian/bilateral filtering (G/BF) with a wavelet thresholding approach is proposed for better image denoising. The performance of the proposed work is compared with some edge-preserving filter algorithms such as a bilateral filter and the Non-Local Means Filter, in terms that objectively assess quality. From the simulation results, it is found that the performance of proposed method is superior to the bilateral filter and the Non-Local Means Filter.

An image Denoising Method Based On Multi Resulation Bilateral Filter

2018 ◽  
Vol 6 (12) ◽  
pp. 448-452
Author(s):  
Md Shaiful Islam Babu ◽  
Kh Shaikh Ahmed ◽  
Md Samrat Ali Abu Kawser ◽  
Ajkia Zaman Juthi
Keyword(s):  
Image Denoising ◽  
Bilateral Filter ◽  
Denoising Method

Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform

Optik ◽  
2018 ◽  
Vol 159 ◽  
pp. 333-343 ◽  
Author(s):  
Sidheswar Routray ◽  
Arun Kumar Ray ◽  
Chandrabhanu Mishra
Keyword(s):  
Image Denoising ◽  
Curvelet Transform ◽  
Bilateral Filter

Echocardiographic image denoising using extreme total variation bilateral filter

Optik ◽  
2016 ◽  
Vol 127 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Nagashettappa Biradar ◽  
M.L. Dewal ◽  
ManojKumar Rohit ◽  
Ishan Jindal
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Bilateral Filter ◽  
Echocardiographic Image

scholarly journals Image Denoising in Spatial and Transform Domains

2021 ◽  
Author(s):  
Mina Sharifymoghaddam
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Weighting Scheme ◽  
Block Matching ◽  
Hard Thresholding ◽  
Weighted Version ◽  
Nonlocal Means ◽  
Processing Step ◽  
Index Measure

Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

Improved Content-Color-Dependent Screening (CCDS): Adaptive Bilateral Filtering and Color-Aware Sobel Edge Detector

2021 ◽  
Vol 2021 (16) ◽  
pp. 252-1-252-7
Author(s):  
Yang Yan ◽  
Jan P. Allebach
Keyword(s):  
Digital Image ◽  
Bilateral Filter ◽  
Bilateral Filtering ◽  
Segmentation Method ◽  
Edge Detector ◽  
Number Of Clusters ◽  
Image Segment ◽  
Continuous Tone

In previous work [1] , content-color-dependent screening (CCDS) determines the best screen assignments for either regular or irregular haltones to each image segment, which minimizes the perceived error compared to the continuous-tone digital image. The model first detects smooth areas of the image and applies a spatiochromatic HVS-based model for the superposition of the four halftones to find the best screen assignment for these smooth areas. The segmentation is not limited to separating foreground and background. Any significant color regions need to be segmented. Hence, the segmentation method becomes crucial. In this paper, we propose a general segmentation method with a few improvements: The number of K-means clusters is determined by the elbow method to avoid assigning the number of clusters manually for each image. The noise removing bilateral filter is adaptive to each image, so the parameters do not need to be tested and adjusted based on the visual output results. Also, some color regions can be clearly separated out from other color regions by applying a color-aware Sobel edge detector.

Using images of noise to estimate image processing behavior for image quality evaluation

2021 ◽  
Vol 2021 (9) ◽  
pp. 217-1-217-6
Author(s):  
Norman L. Koren
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Quality Evaluation ◽  
Bilateral Filter ◽  
Important Application ◽  
Maximum Effect ◽  
Bilateral Filtering ◽  
Fine Detail ◽  
Flat Field ◽  
Insight Into

Noise is an extremely important image quality factor. Camera manufacturers go to great lengths to source sensors and develop algorithms to minimize it. Illustrations of its effects are familiar, but it is not well known that noise itself, which is not constant over an image, can be represented as an image. Noise varies over images for two reasons. (1) Noise voltage in raw images is predicted to be proportional to a constant plus the square root of the number of photons reaching each pixel. (2) The most commonly applied image processing in consumer cameras, bilateral filtering [1], sharpens regions of the image near contrasty features such as edges and smooths (applies lowpass filtering to reduce noise) the image elsewhere. Noise is normally measured in flat, uniformly-illuminated patches, where bilateral filter smoothing has its maximum effect, often at the expense of fine detail. Significant insight into the behavior of image processing can be gained by measuring the noise throughout the image, not just in flat patches. We describe a method for obtaining noise images, then illustrate an important application— observing texture loss— and compare noise images for JPEG and raw-converted images. The method, derived from the EMVA 1288 analysis of flat-field images, requires the acquisition of a large number of identical images. It is somewhat cumbersome when individual image files need to be saved, but it’s fast and convenient when direct image acquisition is available.

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