Methods of Increasing Spatial Resolution of Digital Images with Minimum Detail Loss and its Applications

This paper discusses the techniques that can increase the resolution of a digital image and its applications. The current methods used for increasing the resolution are the interpolation methods, which are Bicubic that is the slowest but produces the best estimation of new pixel values. Bilinear is faster than Bicubic, but does a weaker job. Both Bicubic and Bilinear interpolation result in a blurred image, especially when upsampling. The next method is the Nearest Neighbor, does not use interpolation but simply takes the value of the neighboring pixels and adds new pixels without averaging them, which gives jaggies or stair-case effect. These interpolation methods have downfalls, as it results in blurring of the resized image, especially in areas with sharp lines and distinct changes in color. There are three new methods of increasing image resolution proposed in the paper. The first method will work only on bicolour images like the medical scan images. The raster image of the scan is converted to a vector image where the pixels of the image are converted to geometrical functions and algorithms. The next method involves projecting the image on a plane and then captured by a camera sensor. The resultant image produced by the sensor will be much bigger than input image. The applications of increasing image resolution can provide better detailed zooming and magnification of details in an image, conversion of low-resolution digital images and videos to a higher resolution. The concept of increasing and decreasing the resolution of an image can be used, instead of compression, for sending images over a network.