Abstract: Many imaging scenarios involve an achromatic image (e.g., a panchromatic image or a near-infrared image) and one or more concurrently captured monochromatic images (e.g., RGB images captured through a Bayer filter array), and the compositing of these images through de-mosaicing and/or pan-sharpening to generate a high-resolution color image. However, in many such scenarios, the monochromatic images may exhibit distortion of edge geometry, resulting in artifacts and/or color distortions near visual edges of the composite image. However, such distortions may be absent from the achromatic image, and edge geometry may be represented as an intensity gradient among respective neighborhoods of achromatic pixels. Presented herein are techniques for reducing such distortions in monochromatic images through iterative adjustment of monochromatic pixel intensity to reflect the gradients of the neighborhoods of the corresponding achromatic pixels.

Abstract: Many imaging scenarios involve an achromatic image (e.g., a panchromatic image or a near-infrared image) and one or more concurrently captured monochromatic images (e.g., RGB images captured through a Bayer filter array), and the compositing of these images through de-mosaicing and/or pan-sharpening to generate a high-resolution color image. However, in many such scenarios, the monochromatic images may exhibit distortion of edge geometry, resulting in artifacts and/or color distortions near visual edges of the composite image. However, such distortions may be absent from the achromatic image, and edge geometry may be represented as an intensity gradient among respective neighborhoods of achromatic pixels. Presented herein are techniques for reducing such distortions in monochromatic images through iterative adjustment of monochromatic pixel intensity to reflect the gradients of the neighborhoods of the corresponding achromatic pixels.