Noise Estimation From Digital Step-Model Signal

Laligant, Olivier; Truchetet, Frédéric; Fauvet, Éric

doi:10.1109/tip.2013.2282123

Cited by 17 publications

(11 citation statements)

References 22 publications

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“…The Immerkær’s fast noise variance estimation (FNVE) [9], Khalil’s median absolute deviation (MAD) based noise estimation [39], Santiago’s variance mode (VarMode) noise level estimation [40] and Zoran’s discrete cosine transform (DCT) based noise estimation [12] were chosen as the classical noise level estimation methods. The Olivier’s nonlinear noise estimator (NOLSE) [11], Pyatykh’s principal component analysis (PPCA) based noise esti-mation [17] and Lyu’s noise variance estimation (EstV) [13] were selected as the state-of-the-art noise estimation methods.…”

Section: Resultsmentioning

confidence: 99%

“…In order to further improve the accuracy of Immerkær’s estimation, Yang et al [10] appended adaptive Sobel edge detector to eliminate edges before Laplacian filtering operation. A step signal filter model is presented in [11]. This model is a nonlinear combination of polarized and directional derivatives, and is used to address edges detection and noise estimation.…”

Section: Introductionmentioning

confidence: 99%

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Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Wei

et al. 2019

Sensors

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Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Wei

et al. 2019

Sensors

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show abstract

“…As indicated above, the parameter of this filtering, i.e., the size of the structuring element, is not critical, and there is a wide range of values in which the performance of the filtering is optimum. The mean value of the standard deviation of the speckle noise within the dataset used in this work is estimated using the NOLSE estimator [ 71 ], resulting in

, within the range of

= [0.0030–0.0292]. To evaluate the robustness to speckle noise of the proposed method, we tested the method over the OCT images with synthetically added speckle noise with standard deviation from

(variance from

).…”

Section: Discussionmentioning

confidence: 99%

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

Berenguer-Vidal

Verdú‐Monedero

Morales‐Sánchez

et al. 2021

Sensors

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Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.

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“…Serir et al [8] used the blur effect on real images to measure the image quality. The noise estimation in the digital domain [9] and other quality assessment methods for certain distorted types had also achieved effective results. Hassen et al [10] provided an image sharpness assessment based on local phase coherence.…”

Section: Introductionmentioning

confidence: 99%

Quality Evaluation of DIBR-Synthesized Images Based on Holes and Expanded Regions

Zhao

Wang

et al. 2020

IEEE Access

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With the Depth-image-based rendering (DIBR) technique, virtual views can be drawn at any position between two reference viewpoints. However, DIBR algorithm may introduce various distortions in the synthesized images, such as holes, artifact, and stretching. This paper proposes a quality evaluation method for DIBR-synthesized images by analyzing the holes and expanded regions. First, the holes regions are obtained with the watershed algorithm and used to evaluate the quality of the DIBR-synthesized images. Then, the synthesized image is compared with the reference image in the sub-channel, and the number of different pixel values is used to assess the global quality of synthesized images. Finally, the two measures are pooled to calculate the overall quality score of the DIBR synthesized image. Experimental results show that our scheme can better predict the image quality and performs better in terms of implementation time compared with other schemes. INDEX TERMS DIBR, synthesized image, quality evaluation, watershed algorithm, expanded regions.

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Noise Estimation From Digital Step-Model Signal

Cited by 17 publications

References 22 publications

Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

Quality Evaluation of DIBR-Synthesized Images Based on Holes and Expanded Regions

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