We present a novel local image registration method based on adaptive
filtering techniques. The proposed method utilizes an adaptive filter
to track smoothly, locally varying changes in the motion field
between the images. Image pixels are traversed following a scanning order established by Hilbert curves to preserve the contiguity in the 2-D image plane. We have performed experiments using both simulated images and real images captured by a digital camera. The proposed adaptive filtering framework has been shown by experimental results to give superior performance compared to global 2-D parametric registration and Lucas-Kanade optical flow technique when the image motion consists of mostly translational motion. The simulation experiments show that the proposed image registration technique can also handle small amounts of rotation, scale and perspectivity in the motion field.
Several algorithms have been proposed to enhance the resolution of
a reference image from multiple still images or video that may
be captured/stored in raw or compressed format. This paper provides a
thorough study of how the performance of the projections onto convex sets (POCS) method is affected by camera parameters, quantization of
the pixel-values, motion estimation errors, and quantization in
the DCT-domain (when compressed data is used). Experimental results
are provided to evaluate the practical applicability of super-resolution reconstruction in various scenarios. It has been observed that the quality of the resolution enhancement depends on the quantization of pixel intensity values in the RGB (uncompressed) or YUV (compressed) domains by the video capture device, as well as the accuracy of the estimated motion parameters between successive frames.