Accurate Merging of Sampled Images for Complex Perception Development
Accurate merging of intended pixels can be achieved in high quality using frequency domain techniques. Image merging has been used for many reasons like resolution improvement or for segmentation purposes. We present here a method which could be used in precision engineering and biological applications where more precise prediction is required of a combined phenomenon. When pixels are added, its original property is lost but accurate merging of intended pixels can be achieved in high quality using frequency domain properties of an image. This paper introduces a technique to merge various images which can be used as a simple but effective technique for overlapped view of a set of images and producing reduced dataset for review proposes. Keywords—merging of images, linear integral of frequency density, reduced dataset, compressed view, predictive analysis. I. INTRODUCTION There are several scenarios in image engineering where case exist for multiple object merging under common denominator scenes and later separating these multi objects present in distinctively in intensity seperable layers. Many applications require merging of sampled images for complex perception development. In most cases, for such requirements, images are merged at intensity level. Even though it gives good perception of combined scene of objects, it is found that they are not sufficient to analyze certain case. The main problem is incoherent modulation of intensity arising out of phase properties being lost. In order to compensate these losses, combined phase and amplitude merge is demanded. We present here a method which gives more precise prediction of a combined phenomenon used in precision engineering and biological applications. In the field of image processing, gray level intensity information can be processed easily as the image is stored as a collection of pixels of various colors or intensities. Images are a collection of pixels and each pixel is represented by some values depending on the type of the image, dealing them at intensity level is insufficient for precision management. In gray level digital images, pixels contain intensity information and we perceive and analyze an image based on the changes in shades of color intensities or frequencies. It could be scenarios of night vision for security processing or it could be microscopic vision of biological specimens and culture. Edges are formed due to sudden change in intensity and we can visualize and identify various objects in an image because of their color, shapes and texture. It is the rate of change of intensity values which gives the illusion of an object. Each object in an image generates a unique spectrum of frequencies in frequency domain. An image with many such objects contains a collection of all those frequencies. We are introducing the techniques that gives feasibility for creating accurate high fidelity images through merging. In order to collect all needs of complete information of image, it is necessary and better to merge the frequencies along with phase to get overlapped view of the objects or images. Such methods are required for several practices in astronomy, radiology, body implant predictions, fracture mechanics prediction etc. In literature, Image merging has been used for many reasons like improving resolution or implementing segmentation. A novel image restoration algorithm to deblur the image without