We focus on the study and the enhancement of greedy pattern-based image processing algorithms for the specific purpose of inpainting, i.e., the automatic completion of missing data in digital images and videos. We first review the state of the art methods in this field and analyze the important steps of prominent greedy algorithms in the literature. Then, we propose a set of changes that significantly enhance the global geometric coherence of images reconstructed with this kind of algorithms. We also focus on the reduction of the visual bloc artifacts classically appearing in the reconstruction results. For this purpose, we define a tensor-inspired formalism for fast anisotropic patch blending, guided by the geometry of the local image structures and by the automatic detection of the artifact locations. We illustrate the improvement of the visual quality brought by our contributions with many examples, and show that we are generic enough to perform similar adaptations to other existing pattern-based inpainting algorithms. Finally, we extend and apply our reconstruction algorithms to stereoscopic image and video data, synthesized with respect to new virtual camera viewpoints. We incorporate the estimated depth information (available from the original stereo pairs) in our inpainting and patch blending formalisms to propose a visually satisfactory solution to the non-trivial problem of automatic disocclusion of real resynthesized stereoscopic scenes.