A Discretize-then-Optimize Approach to Super-Resolution Reconstruction and Motion Estimation

The process of recovering a high-resolution (HR) image from a setof distorted (i.e. deformed, blurry, noisy, etc.) low-resolution (LR) imagesis known as super-resolution. Super-resolution problem will requirethe reconstruction of the HR image and estimations of motionbetween LR images. In this study, image reconstruction and motionestimation will be treated as a coupled problem. The proposed algorithmuses an inverse model followed by a discretize-then-optimizeapproach. Preliminary experiments on test data will be presented

Temporally Consistent Edge-Informed Video Super-Resolution (Edge-VSR)

Resolution enhancement of a given video sequence is known as video super-resolution. We propose an end-to-end trainable video super-resolution method as an extension of the recently developed edge-informed single image super-resolution algorithm. A two-stage adversarial-based convolutional neural network that incorporates temporal information along with the current frame's structural information will be used. The edge information in each frame along with optical flow technique for motion estimation among frames will be applied. Promising results on validation datasets will be presented

A Hybrid Landmark and Contour-Matching Image Registration Model

In this manuscript, we propose a novel hybrid Landmark and Contour-Matching (LCM) image registration model to align image pairs. The proposed model uses image contour information to supplement missing edge information in between exact landmarks. We demonstrate that the model circumvent the drawbacks associated with an straightforward application of the Thin Plate Spline (TPS) registration technique.The proposed model provides higher post-registration Dice similarity between the reference and registered template images by improving the image overlap away from major landmarks and visually reduces the appearance of the ''unnatural bending'' typically present in TPS-registered images. We also show that naively increasing the number of landmarks in a TPS model does not always guarantee an accurate registration result. We indicate how the proposed model using even less number of exact landmarks along with additional approximate contour information provided suitable results, as opposed to the TPS model. Lastly, the proposed model produces physically relevant registration results with improved Dice similarity indices even when landmark localization errors are present in data.Overall, the proposed Landmark and Contour-Matching (LCM) model increases the flexibility of the TPS approach especially when only a few landmarks can be defined, when defining too many landmarks leads to high oscillations in the registration transformations, or when the identification of exact landmarks is susceptible to human error

Efficient nonlocal-means denoising using the SVD

Nonlocal-means (NL-means) is an image denoising method that replaces each pixel by a weighted average of all the pixels in the image. Unfortunately, the method requires the computation of the weighting terms for all possible pairs of pixels, making it computationally expensive. Some short-cuts assign a weight of zero to any pixel pairs whose neighbourhood averages are too dissimliar. In this paper, we propose an alternative strategy that uses the SVD to more efficiently eliminate pixel pairs that are dissimilar. Experiments comparing this method against other NL-means speed-up strategies show that its refined discrimination between similar and dissimilar pixel neighbourhoods significantly improves the denoising effect. Index Terms — nonlocal-means, denoising, SVD 1