International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 03 | Mar -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 134 Translation Invariance (TI) based Novel Approach for better De-noising of Digital Images Ranganadh Narayanam * *Assistant Professor, Electronics & Communications Engineering, ICFAI Tech School, IFHE ----------------------------------------------------------------------------****--------------------------------------------------------------------------------- Abstract –Image de-noising is an important image processing task, both as a process itself, and as a component in other processes. Image de-noising means removing unwanted noised form a corrupted image to restore the original image. There are many ways to de-noise an image or a set of data of images, exist. Image De-noising has remained a fundamental problem in the field of image processing. Reducing noise from the original image is still a challenging task for researchers. There are several algorithms and methods each having assumptions, advantages, and disadvantages. Noise can be introduced by transmission errors and compression. In this paper we have introduced an innovative method of Translation Invariant (TI) algorithmic approach for de-noising images to improve SNR. We have applied this algorithm for various Digital Image Processing filters: convolution filter, imaging filter, wiener filter, cross correlation filter, Gaussian filter, order-static filter, wavelet de-noising filter, sliding neighborhood filter. We de-noised the images with all these filters without TI and with TI and we found with TI, the filters are working with better performance than without TI. It is also verified using edge detection by observing the preservation accuracy of edges. In this research we made some useful novel observations, specified as conclusions. Key terms – Image de-noising, edge detection, Translation-Invariance, Image filtering, edge detection, SNR. 1. Introduction to the image de-noising filters: Translation Invariance Approach Digital images play an important role both in daily life applications such as satellite television, magnetic resonance imaging, computer tomography as well as in areas of research and technology such as geographical information systems and astronomy. Data sets collected by image sensors are generally contaminated by noise. Imperfect instruments, problems with the data acquisition process, and interfering natural phenomena can all degrade the data of interest. Furthermore, noise can be introduced by transmission errors and compression. Thus, de-noising is often a necessary and the first step to be taken before the images data is analyzed. It is necessary to apply an efficient de-noising technique [1,2] to compensate for such data corruption. Image de-noising still remains a challenge for researchers [3]. This paper describes different methodologies for noise reduction (or de-noising) giving an insight as to which algorithm should be used to find the most reliable estimate of the original image data given its degraded version. Here we have introduced an innovative approach called Translation Invariant algorithm, for time domain of the images. The basic algorithm has the following steps 1) Shift the noised image for a number of shifts 2) Apply the de-noising using the given filter for each shift 3) Un-shift the de-noised image for each shift 4) Average the results