Super-Resolution for Color Imagery
Technical Report,01 Oct 2016,30 Sep 2017
US Army Research Laboratory, Sensors and Electron Devices Directorate Adelphi
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Super-resolution image reconstruction SRIR can improve image resolution using a sequence of low-resolution images without upgrading the sensors hardware. Here, we consider an efficient approach of super-resolving color images. The direct approach is to super-resolve 3 color bands of the input color image sequence separately however, it requires performing the super-resolution computation 3 times. We transform images in the default red, green, blue RGB color space to another color space where SRIR can be used efficiently. Digital color images can be decomposed into 3 grayscale pictures, each representing a different color space coordinate. In common color spaces, one of the coordinates i.e., grayscale pictures contains luminance information while the other 2 contain chrominance information. We use only the luminance component in the US Army Research Laboratorys ARL SRIR algorithm and upsample the chrominance components based on ARLs alias-free image upsampling using Fourier-based windowing methods. A reverse transformation is performed on these 3 componentspictures to produce a super-resolved color image in the original RGB color space. Five color spaces CIE 1976 L, a, b color space CIELAB, YIQ, YCbCr, hue-saturation-value HSV, and hue-saturation-intensity HSI are considered to test the merit of the proposed approach. The results of super-resolving real-world color images are provided.