Jointly optimizing global and local color consistency for multiple image mosaicking

Li, Li; Mao, Xia; Chi, Liu; Li, Liang; Wang, Hanyun; Yao, Jian

doi:10.1016/j.isprsjprs.2020.10.006

Cited by 17 publications

(4 citation statements)

References 28 publications

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“…Algorithms #5 and #6 are color-correction approaches for image sets (three or more images) and are based on global optimization methods. These are both often used as baseline methods for evaluating color correction approaches for image sets [ 30 , 31 , 54 , 58 , 66 , 67 ]. Algorithm #5 employs an optimization method to determine a global gain compensation to minimize the color difference in the overlapped regions of the images [ 39 ].…”

Section: Resultsmentioning

confidence: 99%

A Sequential Color Correction Approach for Texture Mapping of 3D Meshes

Dal’Col

Coelho

Madeira

et al. 2023

Sensors

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Texture mapping can be defined as the colorization of a 3D mesh using one or multiple images. In the case of multiple images, this process often results in textured meshes with unappealing visual artifacts, known as texture seams, caused by the lack of color similarity between the images. The main goal of this work is to create textured meshes free of texture seams by color correcting all the images used. We propose a novel color-correction approach, called sequential pairwise color correction, capable of color correcting multiple images from the same scene, using a pairwise-based method. This approach consists of sequentially color correcting each image of the set with respect to a reference image, following color-correction paths computed from a weighted graph. The color-correction algorithm is integrated with a texture-mapping pipeline that receives uncorrected images, a 3D mesh, and point clouds as inputs, producing color-corrected images and a textured mesh as outputs. Results show that the proposed approach outperforms several state-of-the-art color-correction algorithms, both in qualitative and quantitative evaluations. The approach eliminates most texture seams, significantly increasing the visual quality of the textured meshes.

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Section: Resultsmentioning

confidence: 99%

A Sequential Color Correction Approach for Texture Mapping of 3D Meshes

Dal’Col

Coelho

Madeira

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…However, if processing patterns and levels do not match the local features, local models can result in new radiometric discrepancies. Some scholars have put forward a combination model that integrates the advantages of the two types of models mentioned above [23][24][25]. The local model is then used to further minimize the local intensity discrepancies after the global model has been used to reduce the global color inconsistency.…”

Section: Introductionmentioning

confidence: 99%

Color Correction and Naturalness Restoration for Multiple Images With Uneven Luminance

Xu,

Hong,

Tong

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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When multiple synthetic aperture radar (SAR) images are stitched together, the intensity disconnects between them can have a significant impact on the mosaic's quality. Many approaches focus on decreasing the intensity differences between images while ignoring the issue of image quality improvement. This study provides an algorithm for color correction and naturalness restoration for multiple images with uneven luminance in order to generate high-quality mosaics.To increase the illuminance component's naturalness, the image is first divided into illuminance and reflectance components, and the illuminance component is subjected to adaptive luminance improvement and contrast enhancement. Using a color consistency optimization approach, the intensity disparities between illuminance components are subsequently minimized. The reflectance and enhanced illuminance components are then combined to produce an improved image. After that, the enhanced image is mosaicked using multi-band blending. Finally, the intensity differences between the enhanced images are further decreased using the block-based Wallis transform based on the mosaic.We assessed the proposed method on 402 Sentinel-1 images covering the majority of China's land area to verify its robustness. When compared to similar algorithms, our strategy reduces the color distance (CD) by about 36.72%, improves the average gradient (AG) by around 89.44%, and increases the patch-based contrast quality index (PCQI) by roughly 32.85%. The experimental outcomes reveal that our approach has considerable advantages in terms of color correction and image quality improvement, both visually and quantitatively.

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“…Then, they used a step-by-step histogram adjustment strategy to correct the image hue in the α and β channels. To optimize local color inconsistency, Li et al [18] used linear models in the superpixel region as a mapping function. They designed and solved a global cost function that accounts for the color correction and gradient preservation of the image.…”

Section: Introductionmentioning

confidence: 99%

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

Hong

Cheng

Tong

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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.

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Jointly optimizing global and local color consistency for multiple image mosaicking

Cited by 17 publications

References 28 publications

A Sequential Color Correction Approach for Texture Mapping of 3D Meshes

A Sequential Color Correction Approach for Texture Mapping of 3D Meshes

Color Correction and Naturalness Restoration for Multiple Images With Uneven Luminance

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

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