Image interpolation is the process of generating a new image utilizing a set of available images. The available images may be taken with a camera at different times, or with multiple cameras and from different viewpoints. Usually, the interpolation problem in the first scenario is called Frame Rate-Up Conversion (FRUC), and the second one view synthesis. This thesis focuses on image interpolation and addresses both FRUC and view synthesis problems. We propose a novel FRUC method using optical flow motion estimation and a patch-based reconstruction scheme. FRUC interpolates new frames between original frames of a video to increase the number of frames, and increases motion continuity. In our approach first, forward and backward motion vectors are obtained using an optical flow algorithm, and reconstructed versions of the current and previous frames are generated by our patch-based reconstruction scheme. Using the original and reconstructed versions of the current and previous frames, two mismatch masks are obtained. Then two versions of the middle frame are generated using a patch-based scheme, with estimated motion vectors and the current and previous frames. Finally, a middle mask, which identifies the mismatch areas of the two middle frames is reconstructed. Using these three masks, the best candidates for interpolation are selected and fused to obtain the final middle frame. Due to the patch-based nature of our interpolation scheme most of the holes and cracks will be filled. Although there is always a probability of having holes, the size and number of such holes are much smaller than those that would be generated using pixel-based mapping. The rare holes are filled using existing hole-filling algorithms. With fewer and smaller holes, simpler hole-filling algorithms can be applied to the image and the overall complexity of the required post processing decreases. View synthesis is the process of generating a new (virtual) view using available ones. Depending on the amount of available geometric information, view synthesis techniques can be divided into three categories: Image Based Rendering (IBR), Depth Image Based Rendering (DIBR), and Model Based Rendering (MBR). We introduce an adaptive version, patch-based scheme for IBR. This patch-based scheme reduces the size and number of holes during reconstruction. The size of patch is determined in response to edge information for better reconstruction, especially near the boundaries. In the first stage of the algorithm, disparity is obtained using optical flow estimation. Then, a reconstructed version of the left and right views are generated using our adaptive patch-based algorithm. The mismatches between each view and its reconstructed version are obtained in the mismatch detection steps. This stage results in two masks as outputs, which help with the refinement of disparities and the selection of the best patches for final synthesis. Finally, the remaining holes are filled using our simple hole filling scheme and the refined disparities. The adaptive version still benefits from the overlapping effect of the patches for hole reduction. However, compared with our fixed-size version, it results in better reconstruction near the edges, object boundaries, and inside the highly textured areas. We also propose an adaptive patch-based scheme for DIBR. The proposed method avoids unnecessary warping which is a computationally expensive step in DIBR. We divide nearby views into blocks, and only warp the center of each block. To have a better reconstruction near the edges and depth discontinuities, the block size is selected adaptively. In the blending step, an approach is introduced to calculate and refine the blending weights. Many of the existing DIBR schemes warp all pixels of nearby views during interpolation which is unnecessary. We show that using our adaptive patch-based scheme, it is possible to reduce the number of required warping without degrading the overall quality compared with existing schemes. / Thesis / Doctor of Philosophy (PhD)