With recent advances in bandwidth, sensor resolution, and UAV technology, image data is being collected in large quantities. A fast, automated, accurate method to register images is needed because human analysis of this data is time consuming and inaccurate. Once registered, images can be utilized more effectively. Applications where image registration algorithms are used include super-resolution, target recognition, and computer vision. Recent research involved registering images with translation and rotation differences using one iteration of the redundant discrete wavelet transform (rDWT). We extend this work by creating a new multiscale transform to register images with translation or rotation differences. Our two-dimensional multiscale transform uses lowpass filtering and the continuous wavelet transform (CWT) to mimic the two-dimensional rDWT, providing subbands at various scales while maintaining the desirable properties of the rDWT. Our multiscale transform produces data at integer scales, whereas the rDWT produces results only at dyadic scales. We also impose exclusion zones to create spatial separation between significant coefficients. This added flexibility improves registration accuracy without greatly increasing computational complexity and permits accurate registration. Our algorithm's performance is demonstrated by registering test images at various rotations and translations, in the presence of additive white noise.