In this paper, we describe a new method for restoring digitized vintage video with film wear artifacts. Such artifacts result in partially or completely missing information. To maximize use of observed data, we cast the problem as that of recovering mattes of artifacts. More specifically, we extract the distributions of artifact color and its fractional (alpha) contribution to the frame. To account for spatial color discontinuity and pixel occlusion or disocclusion, we introduce the alpha-modulatedbilateralfilter. The problem is solved as a 3-D spatio-temporal conditional random field (CRF) with artifact color and (discretized) alpha as states. Inference is done through belief propagation. Results verify the effectiveness of our method. Furthermore, we can produce a synthetically generated vintage footage using extracted artifact information from actual vintage video.