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

Sparse variational regularization for visual motion estimation

The computation of visual motion is a key component in numerous computer vision tasks such as object detection, visual object tracking and activity recognition. Despite exten- sive research effort, efficient handling of motion discontinuities, occlusions and illumina- tion changes still remains elusive in visual motion estimation. The work presented in this thesis utilizes variational methods to handle the aforementioned problems because these methods allow the integration of various mathematical concepts into a single en- ergy minimization framework. This thesis applies the concepts from signal sparsity to the variational regularization for visual motion estimation. The regularization is designed in such a way that it handles motion discontinuities and can detect object occlusions

A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity

The richness of natural images makes the quest for optimal representations in image processing and computer vision challenging. The latter observation has not prevented the design of image representations, which trade off between efficiency and complexity, while achieving accurate rendering of smooth regions as well as reproducing faithful contours and textures. The most recent ones, proposed in the past decade, share an hybrid heritage highlighting the multiscale and oriented nature of edges and patterns in images. This paper presents a panorama of the aforementioned literature on decompositions in multiscale, multi-orientation bases or dictionaries. They typically exhibit redundancy to improve sparsity in the transformed domain and sometimes its invariance with respect to simple geometric deformations (translation, rotation). Oriented multiscale dictionaries extend traditional wavelet processing and may offer rotation invariance. Highly redundant dictionaries require specific algorithms to simplify the search for an efficient (sparse) representation. We also discuss the extension of multiscale geometric decompositions to non-Euclidean domains such as the sphere or arbitrary meshed surfaces. The etymology of panorama suggests an overview, based on a choice of partially overlapping "pictures". We hope that this paper will contribute to the appreciation and apprehension of a stream of current research directions in image understanding.Comment: 65 pages, 33 figures, 303 reference