The fused image will have structural details of the higher spatial resolution panchromatic images as well as rich spectral information from the multispectral images. Before fusion, Mean adjustment algorithm of Adaptive Median Filter (AMF) and Hybrid Enhancer (combination of AMF and Contrast Limited Adaptive Histogram Equalization (CLAHE)) are used in the pre-processing. Here, conventional Principal Component image fusion method will be compared with newly modified Curvelet transform image fusion method. Principal Component fusion technique will improve the spatial resolution but it may produce spectral degradation in the output image. To overcome the spectral degradation, Curvelet transform fusion methods can be used. Curvelet transform uses curve which represents edges and extraction of the detailed information from the image. Curvelet Transform of individual acquired low-frequency approximate component of PAN image and high-frequency detail components from PAN and MS image is used. Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) are measured to evaluate the image fusion accuracy.

In  this  paper,  context  awareness  is  a  promising  technology  that  provides  health care services and a niche  area of big data paradigm. The   drift  in  Knowledge  Discovery  from  Data  refers  to  a  set  of  activities  designed  to refine and  extract  new knowledge from complex  datasets.  The   proposed  model  facilitates  a  parallel  mining  of  frequent item sets for Ambient Assisted Living (AAL) System [a.k.a. Health  Care [System]  of  big  data that  reside   inside  a  cloud  environment.  We  extend  a  knowledge  discovery framework for  processing  and  classifying  the  abnormal  conditions of patients having fluctuations in Blood Pressure (BP) and Heart Rate(HR) and storing  this data  sets  called  Big data  into Cloud to access from  anywhere   when  needed.   This   accessed data is used to compare the new data with it, which helps to know the patients health condition.

This aims to fused image registration and image fusion used to spatial resolution images by principle component analysis method. Digital image processing requires either the full image or a part of image. It will be processed from the user’s point of view like the radius of object. Wavelet technique will improve the spatial resolution to produce spectral degradation in output image. To overcome the spectral degradation, PCA fusion method can be used. PCA uses curve which represent edges and extraction of the detailed information from the image.PAN and MS images are used by individual acquired low frequency approximate component and high frequency detail components in this PCA. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio and Root Mean Square Error are used. The advantages of using digital image processing are preservation of original data accuracy, flexibility and repeatability.