AN ADAPTIVE PRE-PROCESSING FOR COLOR IMAGES WITH THE HYBRID MEDIAN GUIDED [HMG] FILTER FOR
AN EFFICIENT IMAGE COMPRESSION
P.Arul Prabu Dr.S.Rizwana2
Research Scholar Assistant Professor & Head
Erode Arts &Science College Department of Computer Erode Erode Arts &Science College, Erode
Abstract
Image compression technique is one of the stimulating tasks in image processing systems. Lot of image compression techniques are been exists with few problems, leads for pre-processing. Presently, various filters were used for image enhancement. Instead none of them has attained the maximum result among them. Considering such issues we analysed various filters and proposed a hybrid filter. In this paper we used Median filter, Guided filter and the hybrid MG Filter. In this methodology the selected image is processed by the hybridization of median and Guided filter for image enhancement with noise removal. Finally, the proposed HMG filter better result for better enhancement and the performance evaluated with MSE and PSNR. Hence, the new methodology performs well with better enhancement..
Keywords: Image Enhancement, Gaussian filter, Guided Filter, HMG filter, MSE, PSNR.
1. Introduction
Digital image processing refers to process of manipulate images with digital computers. Its applications dominate every field of human survival. The principle processing encompasses signal processing techniques based in the input images.
The major concept behind the Image compression is to reduce the size of the original image in various forms. Thus it provides a way to store more files or images in the given storage area. Another important thing is the time effectiveness in image transportation etc.
The pre-processing of image aims at selectively removing the unwanted contents or external factors present in images. Pre-processing is a method or technique which reduces the noises present in an image. It is accomplished to be better for the forthcoming process such as segmentation, feature extraction, etc.
Pre-processing is a method of suppressing or eliminating / reducing the noise in the images. It consists of various techniques such as binarization and other enhancement techniques. Mostly, various filters are used for efficient pre-processing and image enhancement is the method which improves the superiority of the image.
Various filters are applied for pre-processing of images based the type of noises present in it such as Linear Image Smoothing Filters and Nonlinear Image Filters. The methods and concepts are discussed in the following sections.
2 . Related Work
A method named INRC is applied for grayscale images which are corrupted by dense, low-amplitude, random noise.[1] . [2] Different filters and experimented and compared with the hybrid median filters which does not excessively smooth image details but better than median filter. [3] Another filtering method which can apply for noisy images without any improvement in sharpness. [4] A combined Histogram Equalization and contrast Enhancement Technique using Homomorphic filtering which can be eliminate noises sequentially.[5] Various filter are applied for image enhancement and compared with the median filter which performs well.
[6] An filter designed for noise elimination and restoration is based on MDBPTGMF algorithm specialist in SAP noise. [7] Fuzzy filters combines the edge preservation and smoothing and applied for major image enhancement. [8] A continuous NLF function of image brightness. Estimates an top bounded noise level function by standard deviations.
[9] A method eliminated the noises by estimating the distance among the pixels.
[10] The Gaussian white noise and speckle noise can be easily eliminated using the Nonlinear denoising methods. [11] An overview of image denoising task with conventional sparse representation based denoising algorithm, low –rank based denoising algorithms and recently proposed deep neural networks based approaches.
.
[12] Simulations methods are developed and compare with the median and weighted median filters. [13] Various filtering technique are analysed and experimented with the available spatial filters and the results are compared.
[14] Image rebuilding approaches can preserve image details while suppressing spike noise. The working standard of this technique is introduced and analyzed with different simulation results.
[15] Another approaches which is applied it denoise the image which is occurred in various levels. [16] A method for noise removal using the threshold method to eliminated the noised and provided the reasonable results.
[17] A low frequency filter applied on an image which performs too variation and unable to generate edge smoothening.
[18] A new noise removal use to develop a semi blind based on a steerable wavelet pyramid. [19] In order to avoid assumptions that are made inappropriately on the applied mathematics characteristics of noise, In fact, the non-enhanced image is taken into account to be either free from noise or laid low with nonperceivable levels of noise.
Various methodologies as well as different filters were used in noise elimination are discussed. Since the existing techniques that are not be able to find the optimum solutions thus further it provides a new path to propose new method are discussed in the following sections.
3. Methodology
3.1 Median filter
The filter smoothens by computing are median of the neighbourhood pixels. Here , the image blocks are get sorted and reconstructed as shown in the following figure.
. 1 5 7
2 3 6
This is done by applying the median filter the pixel value which are very different form their neighbouring pixels are eliminated
3.2 Gaussian filter
This filter is applied to eliminate the Gaussian noises and its equation as follows.
…(1)
Based on the above equation (1) the Gaussian filter is separable with normal distribution function.
3.3 Guided Filter
This is a type of filter which computes using a support of one reference filter which guides it for better enhancement. This image can be input image itself or different image
Guided filter produces filtered output ‘q’ by using guidance image ‘I’ and input image
‘p’. Based on the application,.
qi is a local linear transform of guidance image I in a
window ωk centred at pixel k which can be given as 𝑞𝑖=𝑎𝑘𝐼𝑖+𝑏𝑘,∀𝑖∈𝜔𝑘 …(2) Equation (2) is a local linear model which shows
that q has an edge only if I has an edge, because
∇q=a∇I. (3) Here ε is a regularization parameter
which prevents ak from being too large.(4) Solution
of linear coefficients ak and bk for above cost
function is given as
𝑎𝑘=1|𝜔|Σ𝐼𝑖𝑝𝑖−𝜇𝑘𝑝̅𝑘𝑖∈𝜔𝑘𝜎𝑘2+𝜀 𝑏𝑘=𝑝̅𝑘−𝑎𝑘𝜇𝑘 …(5)
4. Proposed Methodology
The developed novel methodology is applied to pre-process the colour image using the hybridization of Median Guided Filter (HMG Filter) on the input image as showing in the following figure.
Input Image I
MG FILTER
MedianFilter
Guided Filter
Pre- processed image with HMG filter
Fig 1. Process flow
The above fig.1 represents the entire pre-processing technique .
Due to the lack of quality issues present in the existing image enhancement methods. We propose a novel filtering technique of hybridization of guided filter with median filter.
Algorithm.
Input: Input query Image Output: Pre-processed image Algorithm:
5. Similarity Measures
The similarity measure PSNR is defined as:
…(6)
Here, MAXI is the maximum pixel value of the image. maximum possible value of MAXI is 2B-1.
5.2 Mean Squared Error (MSE)
The Mean Squared Error (MSE) is defined as:
..(7)
For colour images with three RGB values per pixel, the definition of PSNR is the same except the MSE is the sum over all squared value differences divided by image size and by three.
/pre-processing / Step 1: Begin
Step 2: Choose an query Image from the DB
Step 3: Pre-process as follows i. Noise elimination using
Median filter
ii. Apply Guided filter on the result and Input Image of step 3(1)
iii. Estimate MSE and PSNR Step 4.Store the pre-processed image.
Step 5: Execute step 2 to step 4 for all.
Images in IDB
.
6. Experimentation & Results Sample Images Sample Images
Resulting Images of various filters
Results
Image Name Median Filter GaussianFilter Guided Filter
Proposed HMG Filter
MSE PSNR MSE PSNR MSE PSNR MSE PSNR
IMG1 30.25 32.33 175.21 27.44 51.63 33.60 28.63 33.60 IMG2 82.32 42.33 69.81 69.81 77.15 45.18 7.95 45.18 IMG3 15.33 14.26 25.64 35.71 12.35 37.25 4.15 41.99 IMG4 11.25 36.25 16.32 37.34 9.33 39.25 2.78 43.80 IMG5 112.35 25.35 340.99 23.94 102.36 33.25 87.10 28.76 AVERAGE 50.30 30.10 125.59 38.85 50.56 37.71 26.12 38.67
7. Conclusion
In this paper a new image enhancement method is developed. In order to improve the quality of images there are various filters are available. Here the image is processed with different filters and the newly proposed method which is the Hybridization of Median and Guided filter provides better results and evaluated in terms of MSE and PSNR. By considering all the values we conclude that our propose method is best and an efficient one.
8. References
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