Fusion-Based Multiview Color Correction via Minimum Weight-First-Then-Larger Outdegree Target Image Selection

Color correction for multiview images is a fundamental yet challenging task to generate a color-consistent composite image and has important applications in remote sensing and 3-D vision. In this article, we propose an effective fusion algorithm for multiview color correction using a graph-based adaptive minimum weight-first-then-larger outdegree (AMWLO) strategy to select the next target image. Initially, the given multiview images are stitched using an existing image stitching method. First, we take a modified color distance (MCD) metric to estimate the residual of the overlapped region of each image pair as the weight of the edge. After constructing the graph model of the given multiview images, an AMWLO approach is proposed to select the first target image to be performed for color correction. Then, integrating the histogram equalization (HE) and the joint bilateral interpolation (JBI), the proposed fusion-based method is used to correct the color for the selected target image. After that, the weights between the color-corrected target image and its overlapped images are updated. The above AMWLO-based target image selection, color correction for the selected target image, and edge weight update are repeated until the color correction for all multiview images is completed. To better evaluate the quality performance of the color-corrected stitched multiview images, some modified quality assessments are proposed by taking the overlapping area factor into account. Based on large-scale testing multiview image datasets, the comprehensive experimental results demonstrated the quantitative and qualitative quality merits of our algorithm relative to the state-of-the-art methods.