Presentation on theme: " Image fusion Fusion techniques Literature survey Proposed techniques Mask I. Rectangular mask II. Triangular mask III. Fan shaped mask IV. Strip."— Presentation transcript:
Image fusion Fusion techniques Literature survey Proposed techniques Mask I. Rectangular mask II. Triangular mask III. Fan shaped mask IV. Strip mask Image fusion in transform domain using masking Performance evaluation of image fusion techniques. i. Signal to noise ratio error ii. Root mean square Result of existing technique Comparison of image fusion using different mask. Conclusion References
Image fusion combines multiple images of the same scene into a single image which is suitable for human perception and practical applications. Image fusion is applicable for numerous fields including: defence systems, remote sensing and geosciences, robotics and industrial engineering, and medical imaging.
The most important issue concerning image fusion is to determine how to combine the sensor images. Fusion techniques are commonly divided into two categories: Spatial Domain Techniques: Transform Domain Techniques :
Many fusion rules have been proposed in the existing literature, which are categorized, as follows: Fuse by averaging the corresponding coefficients in each image (‘mean’ rule). Fuse by selecting the greatest in absolute value of the corresponding coefficients in each image (‘max-abs’ rule).
In existing literature Several transforms have already been used such as DCT, DST, DFT, DWT in fusion application. The steps of algorithm based on transform domain technique are summarised as follow : (i) Given images, take the transform of these images. (ii) Obtain the transform coefficients of the images. (iii) Fuse the images by proper selection rule. (iv)Take the inverse transform. (v) Obtain the fused image.
This paper investigates the effect of use of different types of masks in discrete cosine transform (DCT) domain for image fusion applications. Here we have used different types of masks such as rectangular, triangular, strip and fan shaped mask.
Masking is used to retain some portion of one image and some of other image. Here, I have studied four type of mask, which are given below........ i. Rectangular mask ii. Triangular mask iii. Fan shaped mask iv. Strip mask
IMAGE F1 IMAGE F 2 TRANSFORM OF IMAGE F1 TRANSFORM OF IMAGE F2 MASKING OF IMAGE T(F1) COMPLEME -NTARY MASKING OF IMAGE T(F2) ADDING MASKED IMAGE INVERSE OF ADDED IMAGE FUSED IMAGE
The steps of image fusion algorithm in DCT domain by masking technique are summarised as follow : Given two images, F1 and F2. Taking transform(DCT) of the images F1 and F2. Apply mask on both transformed images and added. Taking inverse transform of resultant image. Resultant image is the FUSED image.
Image F1Image F2 Test Images which are used in this paper for image fusion
The acceptable quality for the fused image is set by the receiver of the image which is usually the human observer. Therefore, quality assessments of fused images are traditionally carried out by visual analysis. Other than human visual analysis,we introduce some statistical measures such as the SNR, PSNR, and MSE, RMSE which require an ideal or reference image when applied.
The quality of a signal is often expressed quantitatively with the signal-to-noise ratio defined as: Where Energy signal is the sum of the squares of the signal values. Energy noise is the sum of the squares of the noise samples. where z(m,n) is our estimated signal(fused image) and o(m,n) is original signal.
The RMSE between the reference image R and the fused image F is: where R(i, j) and F(i,j) are the pixel values at the (i, j ) coordinates of the reference image and the fused image, respectively. The image size is I ×J.
In this paper, a new method for image fusion has been proposed using masking technique in DCT domain. The image fusion results of proposed method have been compared with some existing technique. The results show that this method can achieve better quality of fused image using fan shape mask as compared to other mask.
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