Image inpainting is the process of recovering missing or deteriorated data within the digital images and videos in a plausible way. It has become an important topic in the area of image processing, which leads to the understanding of the textural and structural information within the images. Image inpainting has many different applications, such as image/video restoration, text/object removal, texture synthesis, and transmission error concealment. In recent years, many algorithms have been developed to solve the image inpainting problem, which can be roughly grouped into four categories, partial differential equation-based inpainting, exemplar-based inpainting, transform domain inpainting, and hybrid image inpainting. However, the existing algorithms do not work well when the missing region to be inpainted is large, and when there are textural and structural information needed to be recovered. To address this inpainting problem, we propose multiple algorithms, 1) perceptually aware image inpainting based on the perceptual-fidelity aware mean squared error metric, 2) image inpainting using nonlocal texture matching and nonlinear filtering, and 3) multiresolution exemplar-based image inpainting. The experimental results show that our proposed algorithms outperform other existing algorithms with respect to both qualitative analysis and observer studies when inpainting the missing regions of images.

Autonomous driving is an emerging area that has been receiving an increasing amount of interest from different companies and researchers. 3D point cloud map is a significant foundation of autonomous driving as it provides essential information for localization and environment perception. However, when trying to gather road information for map creation, the presence of dynamic objects like vehicles, pedestrians, and cyclists will add noise and unnecessary information to the final map. In order to solve the problem, this thesis presents a novel two-stage model that contains a scan-to-scan removal stage and a scan-to-map generation stage. By designing the new three-branch neural network and new attention-based fusion block, the scan-to-scan part achieves a higher mean Intersection-over-Union (mIoU) score. By improving the ground plane estimation, the scan-to-map part can preserve more static points while removing a large number of dynamic points. The test on SemanticKITTI dataset and Scania dataset shows our two-stage model outperforms other baselines. / Autonom körning är ett nytt område som har fått ett allt större intresse från olika företag och forskare. Kartor med 3D-punktmoln är en viktig grund för autonom körning eftersom de ger viktig information för lokalisering och miljöuppfattning. När man försöker samla in väginformation för kartframställning kommer dock närvaron av dynamiska objekt som fordon, fotgängare och cyklister att lägga till brus och onödig information till den slutliga kartan. För att lösa problemet presenteras i den här avhandlingen en ny tvåstegsmodell som innehåller ett steg för borttagning av skanningar och ett steg för generering av skanningar och kartor. Genom att utforma det nya neurala nätverket med tre grenar och det nya uppmärksamhetsbaserade fusionsblocket uppnår scan-to-scan-delen högre mean Intersection-over-Union (mIoU)-poäng. Genom att förbättra uppskattningen av markplanet kan skanning-till-kartor-delen bevara fler statiska punkter samtidigt som ett stort antal dynamiska punkter avlägsnas. Testet av SemanticKITTI-dataset och Scania-dataset visar att vår tvåstegsmodell överträffar andra baslinjer.