Super-Resolution for Color Imagery

Abstract

Super-resolution image reconstruction (SRIR) can improve image resolution using a sequence of low-resolution images without upgrading the sensor's 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 Laboratory's (ARL) SRIR algorithm and upsample the chrominance components based on ARL's alias-free image upsampling using Fourier-based windowing methods. A reverse transformation is performed on these 3 components/pictures 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.