Abstract
This study proposes a method of blending visible and near-infrared (NIR) images to enhance their edge details and local contrast based on the Laplacian pyramid and principal component analysis (PCA). In the proposed method, both the Laplacian pyramid and PCA are implemented to generate a radiance map. Using the PCA algorithm, the soft-mixing method and the mask-skipping filter were applied when the images were fused. The color compensation method uses the ratio between the radiance map fused by the Laplacian pyramid and the PCA algorithm and the luminance channel of the visible image to preserve the chrominance of the visible image. The results show that the proposed method improves edge details and local contrast effectively.
Highlights
Recent improvements in image sensors have enabled cameras to realize better quality pictures.Many types of image-quality enhancement algorithms, such as high dynamic range (HDR), are available today
We proposed the synthesis of VIS and NIR images
A radiance map was created using the Laplacian pyramid, and the soft-mixing method was employed based on principal component analysis (PCA)
Summary
Recent improvements in image sensors have enabled cameras to realize better quality pictures. Capturing infrared images is advantageous for obtaining details and edge information that are not generally acquired in the VIS spectrum. The latent low-rank fusion algorithm decomposes an input VIS and an infrared image into a low-rank part and a saliency part of each other. The resulting image is obtained using the fused low-rank and saliency parts This algorithm fails to capture the details of the NIR image and does not properly indicate the edge information during color compensation. The values of the weighting map are obtained properly from the PCA results of the VIS and NIR scenes to improve image details. Compared with conventional methods, the proposed method results in less damage to the edge information in the NIR images improved local contrast and details, and better preservation of the color chrominance of the input VIS image. The proposed method preserves more detailed information and enhances the details effectively
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