Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing Images

Abstract

Light and color uniformity is essential for the production of high-quality remote-sensing image mosaics. Existing color correction methods mainly use flexible models to express the color differences between multiple images and impose specific constraints (e.g., image gradient or contrast constraints) to preserve image texture information as much as possible. Due to these constraints, it is usually difficult to correct for the differences in texture between images during image processing. We propose a method that can optimize the luminance, contrast, and color difference of remote-sensing images. In the YCbCr color space, this method processes the chrominance and luminance channels of the image. This is conducive to reducing the influence of the different channels. In the luminance channel, the block-based Wallis transform method is used to optimize the luminance and contrast of the image. In the chromaticity channel, to optimize the color differences, a spline curve is used as a model; the color differences are formulated as a cost function and solved using convex quadratic programming. Moreover, considering the efficiency of our method, we use a graphics processing unit to make the algorithm parallel. The proposed method has been tested on several challenging datasets that cover different topographic regions. In terms of visuals and quality indicators, it shows better results than state-of-the-art approaches.

Color Correction and Naturalness Restoration for Multiple Images With Uneven Luminance

“…This is because the statistical data in the overlapping areas is used by these methods to determine the radiometric mapping relationship between images. Histogram-based [12][13][14][15] and mean-and variance-based [16][17][18] strategies are examples of representative techniques. While the former can process images with similar features well, when the radiometric features fluctuate greatly between images, there is a chance 2 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING that the image texture will be destroyed by altering the image's mean and variance.…”

Research on Color Correction Processing of Multi-Hyperspectral Remote Sensing Images Based on FCM Algorithm and Wallis Filtering

“…In the chromaticity channel, a spline curve was employed as a model to optimize color differences. The color differences were expressed as a cost function and resolved via convex quadratic programming [18].…”