Arnav Bhavsar

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We propose an example-based super-resolution (SR) framework, which uses a single input image and, unlike most of the SR methods does not need an external high resolution (HR) dataset. Our SR approach is based in sparse representation framework, which depends on a dictionary, learned from the given test image across different scales. In addition, our sparse(More)
Shape-from-focus (SFF) is extensively used in image processing for obtaining shape-maps using a sequence of images of a scene captured from same view point with different camera focus settings. Many focus measure operators have been proposed in the literature for SFF applications and their relative performance depends on the camera capabilities and scene(More)
Range-image super-resolution has evolved in recent years to improve the images acquired by low-resolution range-cameras. In this regard, some local filtering based approaches are quite popular as they achieve reasonable quality range-images while maintaining high computational efficiency. In this work, we propose a novel and improved local approach, which(More)
A critical concern with lung 4D-CT is the low superior-inferior resolution, due to the consideration of radiation dose. We propose a resolution enhancement approach that reconstructs missing intermediate slices by exploiting the idea that information lost in one respiratory phase can be found in others, according to the complimentary nature of inter-phase(More)
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