Deep learning based image enhancement for additive manufacturing

A method for enhancing image resolution for sequences of 2-D images of additively manufactured products, the method comprising:

• obtaining a plurality of temporally sequenced low-resolution 2-D images of a product during an in-progress additive manufacturing process;
• obtaining an image enhancement generator previously trained as part of a generative adversarial network, wherein the image enhancement generator is configured to accept input images of a fixed 2-dimensional size;
• selecting one or more tiling maps that subdivide each of the low-resolution 2-D images into a plurality of LR tiles;
• for each of the LR tiles, applying the image enhancement generator to generate a high-resolution 2-D artificial image tile of the product;
• stitching together the high-resolution 2-D artificial image tiles to form a set of high-resolution 2-D artificial layers corresponding to the low-resolution images;
• and
• stacking together the high-resolution 2-D artificial layers to form a 3-D artificial volume of the product,
• wherein the stitching comprises:
• for each pixel included in two or more overlapping regions of the tiling map, generating a respective output image for the respective pixel by computing a respective weighted sum of values in the corresponding high-resolution 2-D artificial image tiles, and
• wherein computing the respective weighted sum comprises:
• associating, for each tile's contribution to the respective weighted sum, a respective weight that is linearly proportional to a distance from a center of the respective tile.