IMAGE ENHANCEMENT USING CONTOURLET TRANSFORM

S.Sathyalakshmi1,K.Karthik2 B.Kowsalya3, K.Janaki sarojini4

1-UG student Department of ECE, 2-Assissant professor of ECE 3-UG student Department of ECE, 4-UG student Department of ECE

P.S.R.Rengasamy College of engineering for women, Sivakasi sathyasg1993@gmail.com1,karthik1312g@mail.com2,

b.kowsalya.1993@gmail.com3,janakiecepro@gmail.com4.

ABSTRACT:

In this paper, an efficient contourlet transform is proposed for Image contrast enhancement. Existing algorithm only based on Wavelets and DCT.There are not suited for all directions but contourlet is anisotropic. The Image fusion is the integration of two or more image to form one composite image. The two images are combined using predefined rule. The input images are separated into four dimensional space based on the directional filtering using contourlet transform. The framework foundation is to enhance the edges. Estimated results compare with the wavelet transform.

INTRODUCTION:

Image enhancement is to process an image so that the result is more suitable than the original image for a special application. This is done by bringing out the detail is dark or hidden or non clearly known in an image. It accentuates or sharpness image features such as edges, boundaries or contrast make a graphic display more helpful for display and analysis.

The visible of still image is low in dark shadow over bright regions and blurred details. Low observable in still images is presented in dark shadows over bright regions and distorted information facts.

One potential manner to resolve the above problem is to apply image enhancement on original single image. Due to design or observational limitation a single image approach usually not succeed in offers the necessary enhancement.

The other approach is to enhance image features by using the details collects from multiple images. For example one can join image from a night vision camera with an image from a visual camera. In this case, the night vision camera is skilled to take image in low light situation but it cannot capture any color information. On the other hand the visual camera can take the color information but the

image capture will have a low contrast and dark shadows. Joining these two images one can successfully capture all the significant information. This process is called image fusion. Since the fused image generally possesses more scene details than any single input image.

Image combination at pixel level is the assimilation of low level information, physical measurements such as pixel intensity. It generates a composite image in which each pixel is determined from a set of corresponding pixels in the various sources.

Wavelet transforms are multiresolution representing of a signals and images. They decompose signals and images into multistage details. The basic functions are used in wavelet transforms are locally supported. They are non zero only one part of the domain represented sharp conversion in image are preserved and showed extremely well in wavelet expansions.

Although the wavelet transform has been proved to be dominant in many signal and image processing applications such as compression, noise, removal, image edge enhancement and features extraction, wavelets are most favorable in capturing the two dimensional singularities found in images. Therefore several transforms have been proposed for images signals that have incorporated

directionality and multiresolutions and hence it efficiently captures edges in natural images.

The contourlet transform is one of the new geometrical image transforms which can efficiently represents images containing contours and textures .This is a stage of subband decomposition followed by a directional transform .In the Contourlet transform, a Laplacian pyramid(LP) is employed in the first stage while Directional filter banks(DFB) are used in the angular decomposition stage.

Method:

Contourlet transform:

For image enhancement, one needs to recover the visual quality of an image with minimal distortion. Wavelet transform have some limitations, since it is not suited to the detection of anisotropic elements. It can only detect edges in horizontal, vertical and 45 degree orientations. But Contourlet transform has better results representing the image salient features such as edges, lines, curves and contours than wavelet transform. It is appropriate for multi-scale edge based color image enhancement. The Contourlet transform consists of two steps which is the Subband decomposition and the directional transform. A Laplacian pyramid in a decomposition of the original image into a hierarchy of images such that each level corresponds to a different bands of image frequencies. It is a first used to confine point discontinuities, then pursued by directional filter banks to link point discontinuity into lineal structure. The overall result is an image extension using basic elements like contour segments; hence the term is contourlet transform being coined. Figure 1 illustrates the contourlet transform.

DIRECTIONAL FILTER LP TRANSFORM

Figure 1

Figure 2 below shows the contourlet filter bank. First, multi scale decomposition by the Laplacian pyramid, and then a directional filter bank is applied to each band pass channel.

Band pass directional

Subband

Band pass directional

Subband

Figure 3 shows a model contourlet transform coefficient of the test images. The images are decomposed into two pyramidal levels, which are then decomposed into four and eight directional subbands.Small coefficients are highlighted black while large coefficients are highlighted white.

(a)

IMAGE

(

(b)

Figure 3 Contourlet transform coefficients of the source images a) Visual image b) Night vision image.

Due to this cascade structure, multiscale and directional decomposition stage in the contourlet transform are self determining of each other. One can decompose each scale into any subjective power of two’s number of directions. The different scales can be decomposing into different number of directions. This feature makes contourlets an exclusive transform.

Figure 4

This diagram showing how wavelets having square supports that can only captures point discontinuities, where as contourlet having elongated support that can capture linear segments of contours and thus can effectively representing a smooth contour with fewer coefficients.

Proposed Framework:

In this paper we are using contourlet transform for image contrast enhancement. At the start, the two input images are decomposed into multiresolution by Contourlet transform. The Contourlet transform generates approximate and detailed coefficient. Then the proposed contrast enhancement function is applied on the transformed coefficients. Lastly pixel based image fusion is utilized. The outcome

image is achieved by applying inverse contourlet transform.

Flow diagram

Contrast enhancement function:

Due to non uniform design, poor contrast images are obtained during attainment. It leads to original image enhancement procedure in order to improve the visual eminence for an enhanced explanation from the image. The ordinary issue in image enhancement method is loss of fine detailed information still good contrast enhancement is completed.

Figure 5

The major process for edge based contrast enhancement is to implement the desired transform by applying the enhancement function to transform coefficients, and then inverse transform to be modified coefficients. Generally used enhancement function is as follows:

Input image

Contourlet

Decomposition

Enhancement function

Pixel based fusion

Reconstruction

Output image

0 ; |b|<T1

X (b) = Sign (b) T2+ a (sign (w (a-v))-

(Sign (w (a+v))) ; T1<| b|< T2

b ;otherwise

Where b [-1, 1], u= u (T3-T2),v є

The proposed enhancement function is much quicker and simpler:

X (b) =a. sign(a).tanh(v.b).(1+w.exp(b^2))

Where b=2.5a/ (t.M),in which u is a coefficient amplitude in transform domain is the magnitude of the maximum coefficient amplitude. In this formula,t*M is a threshold which shows that the coefficient larger than this threshold will be linearly amplified. The parameter v and w together, determine the gain needed in each amplitude interval.

Image fusion:

Image fusion is a process by which several images, are some of their features are combined together to form a single image so that the resultant image contains a more accurate description of the scene than any of the individual source images. In order to create a good understanding of the scene, most importantly in terms of semantic explanations. It increases reliability by the redundant information and capability by complimentary sequence. By integrating information, image fusion can reduce dimensionality. This result in a more proficient storage and faster analysis of the output.

Image fusion can be done at different levels of information representations. The most common once include pixel based and region based. Pixel level fusion is mixing of low level information that is pixel. It generates a composite image in which each pixel is determined from a set of corresponding pixels in a various sources. The region based fusion requires first the extraction of the features contained in the various input sources.

Those features can be identified by characteristics such as size, shape, contrast and texture.

The fusion is thus based on those extracted features and enables the detection of useful features with higher confidence. Image fusion can also be classified as Multimodal and Multifocal depending on the type of sensor involved. Multimodal fusion is when the sensor involved are different, example: Night vision camera and visual camera. On the other hand, Multifocal is when one camera with two or more focal points is used. Here we use the multimodal system for vision clarity. Images from night vision camera are combined with images taken by visual cameras.

In low light condition images taken by the visual cameras have dark shadows and low contrast but it is capable of taking color information. On the other hand night vision cameras are capable of taking images in low light condition but the images contain no color information. Thus combining these two images is the ultimate solutions. Previous fusion, we need to enhance the visual images as important part of it is covering the dark shadows. The advantage of pixel based fusion is that the images used contain the original information and these algorithms are rather easy to implement and time efficient.

Results and Discussion:

The suggested method is applied for several types of images. This method is applied in fire fighting and rescue operation shown below:

(a) (b)

(c) (d)

Figure 6

a) Visual image shows all the information of the house and the smoke, (b) Night vision image reveals the details of the houses and men in the scene, (c) Fused image without enhancement function, (d) Fused image using enhancement function.

(a) (b)

(c) (d)

Figure 7

(a)A man is hardly visible in visual image, (b) Night vision camera; (c) Fused image without enhancement function captures him clearly but not the details of the vegetation, (d) fused image using enhancement function.

This approach can also be used in security surveillance applications shown in figure7.This image is taken in a typical area surveillance scenario for monitoring an outdoor scene in a bad visible condition. A man is walking through the scene behind the bushes.

(a) (b)

( c) (d)

Figure 8

a) Visual image (b) Night vision image (c) Fused image without enhancement function (d)Fused image using enhancement function.

These method are also implemented on a brain computed tomography image and magnetic resonance image. The result are shown in figure8.Medical imaging has become a vital component of a large number of application including diagnosis, research and treatment. In order to support more accurate clinical informations for physicians to deal with medical diagnosis and evaluations, multimodality medical images are needed, such as X-ray, Compute Tomography(CT),Magnetic resonance imaging(MRI) ,Magnetic resonance angiography(MRA) and Positron emission tomography(PET) images.

Table 1 compares the performance of the presented method with wavelet based method using entropy and MSE (Mean square error) between the original and the generated fused images. The numerical results show that the contourlet transform is superior to that of wavelet transform based enhancement method.

Table 1: Performance evalutions result of the test images on figuer6

METHOD ENTROPY MSE Contourlet transform with enhancement function

32

0.0099

Contourlet transform without enhancement function

31

0.0101

Wavelet transform with enhancement function

26.3776

141.6931

Wavelet transform without enhancement function

18.9138

159.3993

Related work:

Essential work has been done on image contrast enhancement based on the contourlet transform. Various algorithms have also been reported for efficient image enhancement.

In an early paper [10], had introduced a Multisensor Image Fusion and Enhancement System for Assisting Drivers in Poor Lighting Conditions. But, there has been no further work to improve the resolution of an image.

In recent paper[11],an image fusion method is proposed based on Fuzzification of region features and Discrete wavelet framework for merging multiple sensor images.But,there has been poor performance evaluations result.

Conclusion and Future work:

In this work we proposed a new method for image contrast enhancement. We used contourlet transform, as a directional non separable transform to enhance edges in the images. A new enhancement function was used for both contourlet and wavelet space. In many applications human perception of the fused images is of fundamental importance. Directional information introduced by the contourlet transform yields better description of all the salient information in both test images. We explained that by wavelet transform we can

effectively enhance the image contrast and also simply reduce noise power. But wavelet transform cannot enhance some directional structures. To better enhance these types of structures, contourlet transform is a good choice. It was shown that by proposed enhancement function or any other proper function such as GAG function, we can enhance some structures for which, wavelet based methods are imperfect. In this paper, we focused on image contrast enhancement based on contourlet transform, but similar principles can be applied to other types of transforms and implementing the region based image fusion algorithm.

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