Analysis and Improvement of SNR using Time Slicing

Noise is a very important factor which in most cases, plays an antagonistic role in the vast field of image processing. Thus noise needs to be studied in great depth in order to improve the quality of images. The quantity of signal in an image, corrupted by noise is generally described by the term Signal-to-Noise ratio. Capturing multiple photos at different focus settings is a powerful approach for improving SNR. The paper analyses a frame work for optimally balancing the tradeoff's between defocus and sensor noise by experimenting on synthetic as well as real video sequences. The method is first applied to synthetic image where the improvement in SNR is studied by the ability of Hough transform to extract the number of lines with respect to the variation in SNR. The paper further experiments on real time video sequences while the improvement in SNR is analyzed using different edge operators like Sobel, Canny, Prewitt, Roberts and Laplacian. The result obtained is further analyzed using different edge operators. The main aim is to detect the edges at different values of SNR which will be a prominent measure of the signal strength as well as clarity of an image. The paper also explains in depth the modeling of noise leading to better understanding of SNR. The results obtain from both synthetic image and real time video sequences elaborate the increase in SNR with the increment in the total number of time slices in a fixed budget leading to clear pictures. This technique can be very effectively applied to capture high quality images from long distances.