Various spacecraft and satellites from the world’s best space agencies are exploring Mars since 1970, constantly with great ability to capture the maximum amount of dataset for a better understanding of the red plane...Various spacecraft and satellites from the world’s best space agencies are exploring Mars since 1970, constantly with great ability to capture the maximum amount of dataset for a better understanding of the red planet. In this paper, we propose a new method for making a mosaic of Mars Reconnaissance Orbiter (MRO) spacecraft payload Context Camera (CTX) images. In this procedure, we used ERDAS Imagine for image rectification and mosaicking as a tool for image processing, which is a new and unique method of generating a mosaic of thousands of CTX images to visualize the large-scale areas. The output product will be applicable for mapping of Martian geomorphological features, 2D mapping of the linear feature with high resolution, crater counting, and morphometric analysis to a certain extent.展开更多
Real-world blind image super-resolution is a challenging problem due to the absence of target high resolution images for training.Inspired by the recent success of the single image generation based method SinGAN,we ta...Real-world blind image super-resolution is a challenging problem due to the absence of target high resolution images for training.Inspired by the recent success of the single image generation based method SinGAN,we tackle this challenging problem with a refined model SR-SinGAN,which can learn to perform single real image super-resolution.Firstly,we empirically find that downsampled LR input with an appropriate size can improve the robustness of the generation model.Secondly,we introduce a global contextual prior to provide semantic information.This helps to remove distorted pixels and improve the output fidelity.Finally,we design an image gradient based local contextual prior to guide detail generation.It can alleviate generated artifacts in smooth areas while preserving rich details in densely textured regions(e.g.,hair,grass).To evaluate the effectiveness of these contextual priors,we conducted extensive experiments on both artificial and real images.Results show that these priors can stabilize training and preserve output fidelity,improving the generated image quality.We furthermore find that these single image generation based methods work better for images with repeated textures compared to general images.展开更多
For better night-vision applications using the low-light-level visible and infrared imaging, a fusion framework for night-vision context enhancement（FNCE） method is proposed. An adaptive brightness stretching method...For better night-vision applications using the low-light-level visible and infrared imaging, a fusion framework for night-vision context enhancement（FNCE） method is proposed. An adaptive brightness stretching method is first proposed for enhancing the visible image. Then, a hybrid multi-scale decomposition with edge-preserving filtering is proposed to decompose the source images. Finally, the fused result is obtained via a combination of the decomposed images in three different rules. Experimental results demonstrate that the FNCE method has better performance on the details（edges）, the contrast, the sharpness, and the human visual perception. Therefore,better results for the night-vision context enhancement can be achieved.展开更多
文摘Various spacecraft and satellites from the world’s best space agencies are exploring Mars since 1970, constantly with great ability to capture the maximum amount of dataset for a better understanding of the red planet. In this paper, we propose a new method for making a mosaic of Mars Reconnaissance Orbiter (MRO) spacecraft payload Context Camera (CTX) images. In this procedure, we used ERDAS Imagine for image rectification and mosaicking as a tool for image processing, which is a new and unique method of generating a mosaic of thousands of CTX images to visualize the large-scale areas. The output product will be applicable for mapping of Martian geomorphological features, 2D mapping of the linear feature with high resolution, crater counting, and morphometric analysis to a certain extent.
文摘Real-world blind image super-resolution is a challenging problem due to the absence of target high resolution images for training.Inspired by the recent success of the single image generation based method SinGAN,we tackle this challenging problem with a refined model SR-SinGAN,which can learn to perform single real image super-resolution.Firstly,we empirically find that downsampled LR input with an appropriate size can improve the robustness of the generation model.Secondly,we introduce a global contextual prior to provide semantic information.This helps to remove distorted pixels and improve the output fidelity.Finally,we design an image gradient based local contextual prior to guide detail generation.It can alleviate generated artifacts in smooth areas while preserving rich details in densely textured regions(e.g.,hair,grass).To evaluate the effectiveness of these contextual priors,we conducted extensive experiments on both artificial and real images.Results show that these priors can stabilize training and preserve output fidelity,improving the generated image quality.We furthermore find that these single image generation based methods work better for images with repeated textures compared to general images.
基金supported by the National Natural Science Foundation of China(No.61231014)the Foundation of Army Armaments Department of China(No.6140414050327)the Foundation of Science and Technology on Low-Light-Level Night Vision Laboratory(No.BJ2017001)
文摘For better night-vision applications using the low-light-level visible and infrared imaging, a fusion framework for night-vision context enhancement（FNCE） method is proposed. An adaptive brightness stretching method is first proposed for enhancing the visible image. Then, a hybrid multi-scale decomposition with edge-preserving filtering is proposed to decompose the source images. Finally, the fused result is obtained via a combination of the decomposed images in three different rules. Experimental results demonstrate that the FNCE method has better performance on the details（edges）, the contrast, the sharpness, and the human visual perception. Therefore,better results for the night-vision context enhancement can be achieved.
