S. Arumuga Perumal

Bio: S. Arumuga Perumal is an academic researcher. The author has contributed to research in topics: Image formation & Peak signal-to-noise ratio. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Fusion of multi focused images using hdwt for machine vision

Abstract: During image acquisition in machine vision, due to limited depth of field of lens, it is possible to take clear image of the objects in the scene which are in focus only. The remaining objects in the scene will be out of focus. A possible solution to bring clear images of all objects in the scene is image fusion. Image fusion is a process of combining multiple images to form the composite image with extended information content. This paper uses three band expansive higher density discrete wavelet transform to fuse two numbers of images focusing different objects in the same scene and also proposes three methods for image fusion. Experimental results on multi focused image fusion are presented in terms of root mean square, peak signal to noise ratio and quality index to illustrate the proposed fusion methods.

Image fusion based on group sparse representation

Abstract: Sparse representation based image fusion has been widely studied recently. However, it’s not popular in some fields for the high time complexity. In this paper, a new image fusion method based on group sparse representation is proposed to overcome this problem. The K-SVD method is utilized to get the sparse representation of the source images. Therefore, it is necessary to find the best size of the group according to its property about time consuming. And there is no need to sparse all the patches once but to sparse some groups simultaneously. Because every group image vectors sparse representation is unique from the others, using the parallel-processing strategy can reduce the time badly. Besides, all dictionaries are learned from local source image vectors, so the quality of the results fused by the group sparse representation method will be better than those fused by the normal sparse representation methods. Compared with four types of state-of-the-art algorithms, the proposed method has the excellent fusion performance in experiments.

Image Fusion on Coloured and Gray Scale Multi Focus Images by using Hybrid DWT-DCT

Abstract: Any piece of information is meaningful only when it is able to convey the content about it. The clarity and quality of information is important. Image Fusion is a technique to improve the quality and quantity of information from a set of images. By the process of image fusion the more information from each of the given images is combining together to generate a resultant image whose quality is maximum to any of the input images. We proposed a Hybrid DWT-DCT method to fuse multi focus images. In this technique we convert the image data from spatial domain to transform domain.. Then decompose the transform data into four parts that is LL, LH, HL, HH part. This decomposition Process again applies in LL part at two levels. After this three level decomposition we combine the input image data by applying average method using DCT and get fused data. This data are in transform domain again convert to spatial domain by applying IDCT and IDWT method and get final fused image with better visual Quality. After getting the result of fused image we compare the quality measure parameters of different technique like PCA, DCT, average pixel, maximum pixel, minimum pixel, HDWT method to hybrid DWT-DCT method. And conclude that the PSNR value and Entropy of fused image have better result as compare to other techniques. Due to this the fused image has better visual Quality as well as more informative data would contain in fused image.