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Journal of applied research and technology

versión On-line ISSN 2448-6736versión impresa ISSN 1665-6423

J. appl. res. technol vol.16 no.2 México abr. 2018



An algorithm for wavelet thresholding based image denoising by representing images in hexagonal lattice

K. M. Jeevana  * 

S. Krishnakumara 

aSchool of Technology and Applied Sciences. Cochin. Kerala India.


The existing method of representation for digital images is using square shaped picture elements called pixels in a rectangular grid. Processing based on hexagonal grid is a new approach in image processing. It has various advantages like symmetry, higher angular resolution, consistent connectivity and higher sampling efficiency. Image processing applications like rotation, scaling, edge detection, and compression in hexagonal domain liave already been discussed by many researchers. In this paper we propose an image denoising scheme in hexagonal lattice using wavelet thresholding method. For the thresholding of wavelet coefficients, modified NeighShrink thresholding method is applied. In NeighShrink method, sub-optimal universal threshold and identical neighboring window size in all wavelet sub-bands are used. However, in the proposed method, instead of sub-optimal universal threshold, an óptima! threshold is determined for every wavelet sub-band by the Stein's Unbiased Risk Estímate (SURE). Denoising is performed on images represented in rectangular grid as well as hexagonal grid using modified thresholding method for comparison. MSE, PSNR and SSIM are used for the performance analysis. The obtained results confirm that the proposed method gives better results than existing algorithms.

Keywords: Wavelet thresholding; Denoising; Hexagonal lattice; Gabor filter; Interpolation


Digital image processing is mainly used for improving or enhancing the pictorial information for human interpretation and processing image data for storage transmission and representation. At present all the images have square shaped pixels and henee all the image processing algorithms are done on the same basis. Processing based on hexagonal grid is a new approach in the field of image processing, because of its various advantages.

Hexagonal image processing is a new developing área of image processing, in which the images are represented by using hexagonal shaped pixels rather than square pixels. Image processing in hexagonal grid is very much advantageous than in the conventional rectangular grid. The advantages include higher angular resolution, consistent connectivity, higher sampling efficiency, etc.

Usually digital images are mapped on square lattice. In hexagonal image processing we change the square lattice to hexagonal lattice by re-sampling the images.

Image denoising is one of the major challenges in the field of image processing and computer visión. Image denoising is the process of estimating the original image by suppressing noise from a noisy image. Usually the noise is introduced in the images during image acquisition (digitization) or during image transmission. Sensor temperature and light levéis are major factors that affect the amount of noise in the images while acquiring images with CCD camera. Also, images are corrupted during transmission. A noisy image can be mathematically expressed as shown in Eq. (1).

INx,y=I x,y+N (x,y) (1)

where I(x,y) is the original image, N(x,y) is the noise, and I N (x,y) is the resulting noisy image. Typical noises usually present in an image are uniform or quantization noise, salt & pepper noise, Gaussian noise, Gamma noise, Rayleigh distribution noise, etc. (Jaiswal, Upadhyay, & Somkuwar, 2004). In this work we focus on Gaussian noise present in an image.

In Gaussian noise the noise having a probability density function (PDF) equal to that of the normal distribution, which is also known as the Gaussian distribution (i.e., the valúes that the noise can take on are Gaussian-distributed). The probability density function 'p' of a Gaussian random variable 'z' is given by

pz=1σ2πe(z-μ)22σ2 (2)

where µ is the mean valué and o is the standard deviation. In the case of images z represents the grey level. A special case is white Gaussian noise, in which the valúes at any pair of times are identically distributed and statistically independent. A lot of algorithms have been proposed to improve the performance of image denoising scheme. All these works are based on the images on rectangular grid. Wavelet thresholding based image denoising by representing images in hexagonal grid using the modified NeighShrink thresholding is mentioned in this work.

The paper is organized as follows. Section 2 is devoted for literature review which includes brief introduction to discrete wavelet transform, wavelet thresholding, and concepts of hexagonal image processing and related works.

In Section 3 we introduce the proposed methodology of image denoising using JeevKrish (Jeev: first four letters of the ñame of the first author, Krish: first five letters of the ñame of the second author) thresholding in hexagonal domain. Results and discussion, and conclusions are given in sections 4 and 5, respectively.



A wavelet is a waveform of an effectively limited duration that has an average valué of zero. A function ψ (x) can be called a wavelet if it possesses the following properties:

1.- The function intégrate to zero, or equivalently, its Fourier transform denoted as ψ (ω) is zero

-ααψxdx=0 (3)

2.- It is square integrable, or equivalently, has finite energy

-αα|ψ x|2dx<α (4)

Eq. (3) suggests that the function is either oscillatory or has a wavy appearance and Eq. (4) implies that most of the energy in ψ (x) is confined to a finite interval or in other words ψ (x) has good space localization.

The discrete wavelet transform (DWT) is obtained by filtering the signal through a series of digital filters at different scales. The scaling operation is done by changing the resolution of the signal by the process of subsampling. In the computation of DWT, the input image is decomposed into low-pass and high-pass sub-bands, i.e., each decomposition stage consists of a low-pass filter (LPF) and a high-pass filter (HPF). When the signal passes through these filters, it splits into two bands. The LPF, which corresponds to an averaging operation, extracts the coarse information of the signal. The HPF, which corresponds to a differencing operation, extracts the detailed information of the signal. The output of the filtering operation is then decimated by two. In the case of images (two-dimensional signal), first, the image is filtered along the row and decimated by two.

Then it is followed by filtering the sub-image along the column and decimated by two. This operation splits the image into four bands namely LL (Low frequency or approximation coefficient), LH (vertical details), HL (horizontal details), and HH (diagonal details) as shown in Figure 1.

Fig. 1 First Level Wavelet Decomposition. 


Denoising by thresholding in wavelet domain was developed by Donoho and Johnstone (1995). In wavelet domain, large coefficients correspond to the signal and small ones represent mostly the noise. The thresholding of wavelet coefficients is done by either 'Hard' or 'Soft' thresholding function.

In hard thresholding (Figure 2(a)) the wavelet coefficients below the threshold 'T' are made zero and coefficients above the threshold are not changed. If 'x' and 'y' represent the input and output respectively, then the hard thresholding is given by the equation

x=Thy-T=y,if|y|0,otherwiseT (5)

Fig. 2 (a) Hard Thresholding, (b) Soft Thresholding First 

In the case of soft thresholding (Fig. 2(b) ), the wavelet coefficients are shrunk towards zero by a threshold valué 'T'. It can be represented by the equation

x=Tsy,T={y-T, if y Ty+T, if y -T0, otherwise (6)

Selection of low valué of threshold may produce a result which is near to the input, but the result still may be noisy. Larger valué of threshold produces an output with large number of zero coefficients. The result of this selection is a very smooth signal. The smoothness suppresses the details and edges of the original image and causing blurring and ringing artifaets. Some of the existing methods in the literature for estimating the threshold are described below.


This technique consists of applying the soft thresholding operator using the universal threshold proposed by Donoho and Johnstone (1995) and this threshold is given by the equation

Tvisu=σn2logL (7)

where σn2 is the noise variance of Adaptive White Gaussian Noise (AWGN) and 'L' represents the total number of pixels in the image. This method gives a highly smoothed reconstruction of the noisy image, but we have to compromise many of the important features of the image as the threshold tends to be high for large valúes of 'L' and this may eliminate the signal coefficient along with noise.


Thresholding method that minimizes the Stein Unbiased Risk Estimator (SURE) developed by Donoho and Johnstone (1995) is known as SureShrink wavelet thresholding technique. This method select distinct threshold for each sub-band of each level of the wavelet tree and henee it is known as an adaptive method for selecting threshold. The SureShrink threshold Tsure is expressed as

Tsure=min(t,σ2logL (8)

where 't' represents the valué that minimize the Stein Unbiased Risk Estimator (SURE), σ2 is the noise variance and L is the size of the image.


The threshold derived in a Bayesian approach is given by the equation

TB=σn2σF (9)

where σn2 is the estimated noise variance of AWGN by robust median estimator and σF is the estimated signal standard deviation in wavelet-domain. The robust median estimator is given by the equation

σn=Median {Yij}0.6745, YijSubband (10)


Chen, Bui, and Krzyzak (2004) proposed a new wavelet thresholding method in their paper titled Tmage denoising using neighbouring wavelet coefficients' called NeighShrink. It is the modification of wavelet denoising scheme for ID signal by incorporating neighboring coefficients in the thresholding process proposed by Cai & Silverman (2001). In NeighShrink method, the wavelet coefficients are thresholded according to the valué of the squared sum of all the valúes of wavelet coefficients in the neighborhood window. The window size can be taken as 3X3, 5X5, 7X7, etc. Init is already reported that 3X3 window gives better results. The shrinkage for NeighShrink is expressed as

βij=1-Tu2Sij2 (11)

where Sij2 is the squared sum of all wavelet coefficients in the selected window and T u is the universal threshold. If Sij2 is less than or equal to Tu2 , then the wavelet coefficient (Cij) to be modified is set to zero. Otherwise it is modified according to

Cij=Cijβij (12)

Some other thresholding methods are SmoothShrink method proposed by Mastriani and Giraldez (2007) for images corrupted by speckle noise. Another method called BiShrink, based on non-Gaussian bivariate distributions model proposed by Sendur & Selesnick (2002a, 2002b). Shengqian, Yuanhua, and Daowen (2002) proposed an adaptive shrinkage denoising scheme using neighborhood characteristics and they claimed that their scheme produced better results than the results produced by Donoho (1995) and Donoho et al. (1995).


The photoreceptors (Rods & Cones) of human retina closely resembré to hexagonal structure (Curcio, Sloan. Kalina, & Hendrickson, 1990). Figure 3 shows the arrangement of rods & cones in human retina. Since the cells in human retina have hexagonal structure, the image developed in the retina has hexagonal shaped pixels. In addition, hexagonal geometry has some advantages like higher sampling efficiency, consistent connectivity and higher angular resolution. Due to these reasons many researchers have studied the possibility of representing digital images with hexagonal pixels (Barun, Pooja & Kuldip, 2014; Middleton & Sivaswamy, 2005; Veni & Narayanankutty, 2011). But the non-availability of hardware for capturing and displaying hexagonal based images limits the use of hexagonal image structure for further processing. Since there is no hardware for capturing the hexagonal images, conversión has to be done from square to hexagonal image before hexagonal-based image processing.

Fig. 3 Arrangement of rods and cones in human retina. 

There have been several ways to simúlate a hexagonal grid on a regular rectangular grid. The common simulations methods are re-sampling the square lattice to the hexagonal lattice, mimic hexagonal pixels using square pixels, pseudo hexagonal pixel, and virtual hexagonal structure.

One method of representing hexagonal lattice using the simulation method - resampling the square lattice to the hexagonal lattice - is altérnate pixel suppressal method (Sankar, Sanjay & Rajan, 2004). Hexagonal grid image based on altérnate pixel suppressal method can be obtained from the conventional image by alternatively suppressing rows and columns of the existing rectangular grid and sub-sampling it. All the other pixels of the rectangular grid which do not have any correspondence with the hexagonal counterparts are suppressed to zero.

While processing this sub-sampled image the suppressed pixels are not considered in computation. The sub-sampled hexagonal grid is shown in Figure 4.

Fig 4 (a) Rectangular grid an (b) Simulated hexagonal grid obtained using alternate pixel suppression. 

Image processing applications like image rotation, edge detection, etc. in hexagonal lattice have already been discussed by many researchers. Middleton and Jayanthi Sivaswamy (2001) proposed an edge detection method in a hexagonal image processing framework. In this work the authors conclude that the hexagonal framework gives better results for edge detection. Veni, and Narayanankutty (2011), Vidya et al. (2009) and Vidya (2011) also analyzed the performance of edge detection on hexagonal sampling grid. Comparisons of image alignment on hexagonal and square lattices were performed by Shima, Sugimoto, & Okutomi (2010). Azam, Anjum, & Javed (2010) proposed discrete cosine transform based method for face recognition in hexagonal images. Wavelet based image compression in hexagonal domain was proposed by Jeevan & Krishnakumar (2014) in the paper titled 'Compression of images represented in hexagonal lattice using wavelet and Gabor filter'. In all these works, the authors confirmed that the hexagonal representation gives better results.

In this paper we propose an image denoising method in hexagonal lattice using wavelet thresholding method. Modified NeighShrink method, which is explained in the next section, is used for thresholding.


An image denoising method using modified NeighShrink thresholding method named as JeevKrish thresholding is proposed in our work. Although the work proposed is image denoising, we are trying to confirm that the hexagonal representation is better than the normal square pixel representation of images. We consider images corrupted with Gaussian noise. Figure 5 shows the block diagram of the proposed method.

Fig. 5 Block Diagram for the Proposed Method 

Due to the non-availability of hardware for acquiring hexagonal images, rectangular grid to hexagonal grid conversión has to be done before performing hexagonal image processing. In this work we use altérnate pixel suppressal method, which is explained in Section 2.3 for simulating the hexagonal grid from rectangular grid. During the conversión of images from rectangular lattice to the hexagonal lattice, considerable loss in image quality was observed. So, image reconstruction through interpolation is used. Interpolation using Gabor filter is used in this work.

Gabor filter (Gabor, 1946; Vidya, 2011) is the only filter with orientation selectivity that can be expressed as a sum of two separable filters. In the spatial domain, a 2D Gabor filter is a Gaussian kernel function modulated by a sinusoidal plañe wave. The foliowing equations represent the 2D-Gabor function which was proposed by Daugman (1985). Equations 13 and 14 represent the real and imaginary part of the function, respectively.

gλθσγφx,y=x'2+γ2y'22σ2cos2πx'λ+φ (13)

gλθσγφx,y=x'2+γ2y'22σ2sin2πx'λ+φ (14)

where x' = xcosθ + ysinθ and xy' = -xsinθ + ycosθ

The arguments 'x' and 'y' specify the position of 'x' and 'y' coordínate of the image and sigma (σ), gamma (γ), lambda (λ), theta (ɵ), and phi (φ ) are parameters as follows. The standard deviation (σ) of the Gaussian factor determines the size of the receptive field. The parameter gamma (γ) named as aspect ratio, specifies the ellipticity of the Gaussian factor. It has been found to vary in a limited range of 0.23 < γ < 0.92. The parameter lambda (λ) is the wavelength and 1/ λ the spatial frequency of the cosine factor in Eq. (13). The ratio σ / λ determines the spatial frequency bandwidth.

The interpolation is done using Gabor filter in the following manner. Hexagonal sampled grid has directional symmetry in 0o, 60°, and 120° orientations. Due to these three axes of symmetry of hexagonal grid, we select three different orientation of Gabor filter along in 0o, 60°, and 120°, and the filtering is done in these three orientations (Jeevan et al., 2014). The three filtered images in these three orientations are then superimposed to get the interpolated image.

The filtered image is then decomposed using wavelet to get four sub-bands namely LL, LH, HL and HH. LL is the low frequency sub-band and most of the image details are present in this sub-band. LH, HL, and HH contain high frequency components and henee most of the noises are present in these bands. Therefore, thresholding is done on all the coefficients in LH, HL, and HH sub-bands. This thresholding is done using JeevKrish method which is explained below.

Let Cij be the wavelet coefficients in the sub-band.

Select the neighborhood window as shown in Figure 6 with size 3X3 and C as the center element.

Fig. 6 3X3 Neighborhood Window with Cij, the coefficient to be thresholded, at the center. 

1. Calcúlate







Where Tsure is the threshold obtained using SureShrink.

As explained in Section 2.2 SureShrink is the thresholding method that minimizes the Stein Unbiased Risk Estimator (SURE). This method selects distinct threshold for each sub-band of each level of the wavelet tree and get an optimal threshold valué compared to the universal thresholding. The SureShrink threshold Tsure is expressed as


where 't' represents the valué that minimize the Stein Unbiased Risk Estimator (SURE), σ2 is the noise variance and L is the size of the image. So the thresholding factor for JeevKrish thresholding can be expressed as

i,j=1-Tsure2Sij2 (15)

Therefore, the shrinkage rule is; if Si,j 2 Tsure2, the coefficient Ci,j is set to zero. Otherwise Ci,j=Ci,ji,j where, i,j- is the proposed shrinkage factor. Using this shrinkage rule, thresholding is done on each wavelet coefficient by selecting 3X3 neighborhood window. Soft thresholding is performed in this process. Finally perform inverse wavelet transform on the thresholded wavelet coefficient to get the denoised image.

The proposed denoising algorithm can be summarized as follows:

  1. Convert the noisy image in rectangular grid to hexagonal grid using altérnate pixel suppressal method.

  2. Perform Gabor filter interpolation on hexagonal grid image.

  3. Perform wavelet decomposition on the image obtained after step 2.

  4. Threshold the wavelet coefficients in the sub-bands LH, HL, and HH using the proposed JeevKrish method choosing 3X3 neighborhood window size.

  5. Perform inverse wavelet transform on the thresholded wavelet coefficient to get the denoised image.

This method gives better results compared to other shrinkage methods like VisuShrink, SureShrink, and NeighShrink and also it is better than the MATLAB denoising function 'wdencmp'.


The proposed JeevKrish denoising method is performed on more than hundred images. This paper gives importance for performing denoising using proposed method in hexagonal domain. The hexagonal grid is obtained using altérnate pixel suppressal method and interpolation using Gabor filter. The Gabor filtering is done in three different orientations along in 0", 60", and 120°. This filtering helps to remove noises in high frequency región. The test results for all these images in terms of Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) indícate that the denoising performance is better in hexagonal domain. In addition to MSE and PSNR, we also used structural similarity (SSIM) for analyzing the performance. SSIM also indícate that the performance is better in hexagonal domain. The performances of proposed method on conventional images are better than the denoising using NeighShrink, VisuShrink and SureShrink_methods. It also performed better than the MATAB de-noising function 'wdencmp' and if we represent the images in hexagonal lattice, the performance of the proposed method again improves and gives better Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). These results confirm that the hexagonal representation is better than the conventional rectangular lattice.

In NeighShrink, Si,j 2 obtained is compared with Tu2, where T u is the universal threshold which does not deals with minimizing MSE and henee eliminate many coefficients. But in the case of JeevKrish thresholding, the proposed method Si,j 2 is compared withTsure2, the SureShrink threshold. It is based on Stein's Unbiased Risk Estimator (SURE) and it minimizes mean squared error and gives an optimal threshold valué which is better than t u . In JeevKrish method, each coefficient in each sub-band is thresholded using this optimal threshold valué. We are also considering the effect of neighbor coefficients for thresholding the coefficient C i,j . So this method gives better results than other methods.

MSE and PSNR, two commonly used measures for quantifying the error between images, are used for the performance analysis. MSE indicate the average difference of the pixel throughout the image. If MSE is higher the difference of the pixel between the original and the processed image is also higher. The MSE between two images P and Q is defined by

MSE=1Nij(Qij-Pij)2 (16)

where the sum over 'i' and 'j' denotes the sum over all pixels in the images, and 'N' is the total number of pixels in each image. The PSNR between two images is given by

PSNR=10log102552MSE (17)

SSIM is another parameter used for the performance analysis. It is used for measuring similarity between two images. "The MSE or PSNR estímate absolute errors on the other hand, SSIM is a perception-based model that considers image degradation as perceived change in structural information, while also incorporating important perceptual phenomena, including both luminance masking and contrast masking terms (Wang Zhou et al, 2004).

The obtained PSNR for five different images are represented in Table 1. Graphical representation of 'MSE vs. σ ' for the image 'elephant.jpg' is shown in Figure 7. Experiments are performed on more than hundred images. Figure 8 (a - f) and Figure 9(a - f) shows the test result for two different images 'car.jpg' with σ = 30 and 'lena.png' with σ = 40. Table 2 gives a comparison of SSIM for conventional square grid images and hexagonal grid images.

Table 1 MSE&PSNR obtained for five different images for different σ 

Noise Level (σ) Test Images MATALAB Function (wdencmp JeevKrish (Proposed Method)
Rectangular Grid Image Rectangular Grid Image Hexagonal Grid Image
10 car.jpg 17.576 35.6816 14.7061 36.4558 2.6659 43.8723
elephant.jpg 30.2087 33.3295 18.1072 35.5523 9.6699 38.2766
flower.jpg 20.141 35.09 17.6277 35.6688 2.2797 44.5521
barbara.png 40.9523 32.008 25.7547 34.0222 13.0611 36.971
lena.png 23.5426 34.4123 19.979 35.1251 3.7642 42.3741
20 car.jpg 43.2053 31.7754 42.5185 31.845 3.155 43.1409
elephant.jpg 45.6756 31.534 42.1588 31.8819 11.7425 37.4332
flower.jpg 44.7033 31.6274 44.4646 31.6507 3.1774 43.1101
barbara.png 57.7775 30.5132 52.8348 30.9016 14.3789 36.5536
Lena.png 46.6498 31.4423 46.612 31.4458 4.6889 41.4201
30 car.jpg 63.6594 30.0922 63.6157 30.0952 4.0735 42.0311
elephant.jpg 57.7272 30.517 55.9205 30.6551 13.9895 36.6728
flower.jpg 65.0948 29.9943 64.7502 30.0184 5.2854 40.9
barbara.png 73.0098 29.497 70.8848 29.6253 15.7914 36.1466
lena.png 66.3655 29.9114 66.0229 29.9339 5.8342 40.471
40 car.jpg 77.052 29.263 76.9681 29.2677 5.9666 40.3735
elephant.jpg 67.7233 28.8234 65.0718 29.9969 17.9162 35.5983
flower.jpg 78.336 29.1912 78.0755 29.2057 9.3387 38.4279
barbara.png 82.6824 28.9567 81.5699 29.0155 18.023 35.5725
lena.png 79.1361 29.1471 78.3899 29.1882 7.487 39.3877
50 car.jpg 85.8141 28.7952 85.8648 28.7927 6.1066 40.2728
elephant.jpg 73.2895 29.4804 72.1936 29.5458 22.0585 34.695
flower.jpg 86.9257 28.7393 87.3004 28.7206 15.3704 35.9323
barbara.png 90.0086 28.588 90.0932 28.5839 22.3416 34.6397
lena.png 86.9196 28.7396 87.0679 28.7322 10.2045 38.0429

Fig 7 MSE vs σ for the image 'elephant.jpg' 

Fig. 8 (a) Original image (b) Noisy Image a =30, (c) Noisy image in Hexagonal lattice (d) Image after interpolation using Gabor filter (e) De-noised image in Rectangular lattice (f) De-noised image in Hexagonal Lattice. 

Fig. 9 (a) Original image (b) Noisy Image a =40, (c) Noisy image in Hexagonal lattice (d) Image after interpolation using Gabor filter (e) De-noised image in Rectangular lattice (f) De-noised image in Hexagonal Lattice. 

A statistical analysis is also done for the performance comparison. Analysis of Variance (ANOVA) is used for the analysis of the methods presented in this paper. Data sets used for the statistical analysis and the details of analysis are presented in Table 3, 4 and 5 respectively. between the methods and also it is seen that the average From the table it is clear that the 'F' valué is greater than valué of PSNR is greater for the proposed method. So we 'F-Critical' which indícate there is significant difference can conclude that our method gives better results.

Table 2 SSIM obtained for Square Grid and Hexagonal Grid Images 

Test Images Noise Level (σ) is 20 SSIM
Rectagular Grid Image Hexagonal Grid Image
Imagel 0.4869 0.6856
Image2 0.4771 0.585
Image3 0.5403 0.6496
Image4 0.5537 0.5818
Image5 0.5102 0.643
Image6 0.5228 0.557
Image7 0.3889 0.6931
Image8 0.3855 0.673
Image9 0.4935 0.5865
ImagelO 0.393 0.6008

Table 3 Data Sets Used for ANOVA. 

Denoising Using JeevKrish (proposed) Method in Hexagonal Grid Image Denoising Using JeevKrish (proposed) Method in Rectangular Grid Image Denoising Using MATLAB Function (wdencmp)
PSNR for 5 Different test Images (Noise Level σ=10) 43.8723 36.4558 35.6816
38.2766 35.5523 33.3295
44.5521 35.6688 35.09
36.971 34.0222 32.008
42.3741 35.1251 34.4123

Table 4.Analysis of Variance (ANOVA) Summary 

Groups Count Sum Average Variance
Denoising Using JeevKrish (proposed) Method in Hexagonal Grid Image 5 206.0461 41.20922 11.54664
Denoising Using JeevKrish (proposed) Method in Rectangular Grid Image 5 176.8242 35.36484 0.794471
Denoising Using MATLAB Function (wdencmp) 5 170.5214 34.10428 2.137283

Table 5 Analysis of Variance (ANOVA) Table of ANOVA 

Source variation SS df MS F P-value F crit
Between Groups 143.7099 2 71.85497 14.88873 0.000562 3.885294
Within Groups 57.91357 12 4.826131
Total 201.6235 14 To


We simulated the proposed method and it has been found that the images represented in hexagonal domain gives better results than conventional images. Also the proposed method is better than the denoising function 'wdencmp' used in MATLAB. MSE, PSNR and SSIM are found to be better in hexagonal domain. Combining wavelet and processing of images in hexagonal grid, we achieved a better result than conventional methods, because hexagonal wavelet includes the advantage of hexagonal grid along with the wavelet. Also it gives the missing córner stone of artificial human visual system.


The authors have no confliets of interest to declare.


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*Corresponding author. E-mail (Jeevan K. M.). Peer Review under the responsibility of Universidad Nacional Autónoma de México. htt.p://

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