Noise reduction and image smoothing using gaussian blur.

The main goal of this project is to design a filter to smoothen the given image based on Gaussian blur technique. Image Blurring or image smoothening is a technique of averaging the group of pixels in order to reduce noise and sharpness at the edges. Noise reduction is one of the major concerns in the image analysis and computer vision application industry. The main goal of noise reduction is to remove unprofitable information that may corrupt the image. This can be achieved by numerous techniques, such as Median filtering, Gaussian filtering, averaging, applying Fourier transformation and many more. Edges in an image are the outline that details the structure of an object in the image. Blurring is in fact reducing the edges and making the transition from one color to the next color in a smooth manner. The blurring or image smoothening effect can be achieved by choosing an appropriate nonlinear filter kernel, which performs the averaging in a selected neighborhood of pixels and normalizes the pixel value. The filter kernel coefficients change their values according to the image structure, which is to be smoothened. Adaptive smoothing is a nonlinear process, which reduces the noise but still preserves the valuable features of the image. There are few trade-offs that have to be taken into consideration in order to bring up a product in the market in which size and performance are crucial. Size refers to the actual hardware required to implement the design whereas performance refers to how fast the system can perform the operation. In this project, I have mainly concentrated on the first factor "size", making use of limited hardware trading off with the performance.