Depth Superresolution using Motion Adaptive Regularization

Spatial resolution of depth sensors is often significantly lower compared to that of conventional optical cameras. Recent work has explored the idea of improving the resolution of depth using higher resolution intensity as a side information. In this paper, we demonstrate that further incorporating temporal information in videos can significantly improve the results. In particular, we propose a novel approach that improves depth resolution, exploiting the space-time redundancy in the depth and intensity using motion-adaptive low-rank regularization. Experiments confirm that the proposed approach substantially improves the quality of the estimated high-resolution depth. Our approach can be a first component in systems using vision techniques that rely on high resolution depth information

Patch-Wise Adaptive Weights Smoothing in R

Image reconstruction from noisy data has a long history of methodological development and is based on a variety of ideas. In this paper we introduce a new method called patchwise adaptive smoothing, that extends the propagation-separation approach by using comparisons of local patches of image intensities to define local adaptive weighting schemes for an improved balance of reduced variability and bias in the reconstruction result. We present the implementation of the new method in an R package aws and demonstrate its properties on a number of examples in comparison with other state-of-the art image reconstruction methods

Optimizing the usage of 2D and 3D transformations to improve the BM3D image denoising algorithm

Image denoising is one of the most important pre-processing steps prior to wide range of applications such as image restoration, visual tracking, image segmentation, etc. Numerous studies have been conducted to improve the denoising performance. Block Matching and 3D (BM3D) filtering is the current state-of-the-art algorithm in image denoising and can provide better denoising performance than other existing methods. However, still, there is scope to improve the performance of BM3D. In this thesis, we have pointed out some of the significant aspects of this algorithm which can be improved and also suggested some approaches to get better denoising performance. We have suggested using an adaptive window size rather than the fixed window size. In addition, we have also suggested using gradient image in the blockmatching step to better facilitate the similar patch searching. Experimental results show that our suggested approaches can produce better results than BM3D irrespective of the types of image