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Single Image Superresolution Based on Gradient Profile Sharpness

机译：基于梯度轮廓锐度的单图像超分辨率

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摘要

Single image superresolution is a classic and active image processing problem, which aims to generate a high-resolution (HR) image from a low-resolution input image. Due to the severely under-determined nature of this problem, an effective image prior is necessary to make the problem solvable, and to improve the quality of generated images. In this paper, a novel image superresolution algorithm is proposed based on gradient profile sharpness (GPS). GPS is an edge sharpness metric, which is extracted from two gradient description models, i.e., a triangle model and a Gaussian mixture model for the description of different kinds of gradient profiles. Then, the transformation relationship of GPSs in different image resolutions is studied statistically, and the parameter of the relationship is estimated automatically. Based on the estimated GPS transformation relationship, two gradient profile transformation models are proposed for two profile description models, which can keep profile shape and profile gradient magnitude sum consistent during profile transformation. Finally, the target gradient field of HR image is generated from the transformed gradient profiles, which is added as the image prior in HR image reconstruction model. Extensive experiments are conducted to evaluate the proposed algorithm in subjective visual effect, objective quality, and computation time. The experimental results demonstrate that the proposed approach can generate superior HR images with better visual quality, lower reconstruction error, and acceptable computation efficiency as compared with state-of-the-art works.
机译：单图像超分辨率是一种经典的主动图像处理问题，旨在从低分辨率输入图像生成高分辨率（HR）图像。由于此问题的严重不确定性，必须有一个有效的图像先验才能使问题解决并提高生成图像的质量。本文提出了一种新的基于梯度轮廓清晰度（GPS）的图像超分辨率算法。 GPS是边缘锐度度量，其是从两个梯度描述模型即三角形模型和高斯混合模型中提取的，用于描述不同种类的梯度剖面。然后，统计研究不同图像分辨率下的GPS转换关系，并自动估算该关系的参数。基于估计的GPS变换关系，针对两个剖面描述模型，提出了两个梯度剖面变换模型，可以在剖面变换过程中使剖面形状和剖面梯度幅值和保持一致。最后，从变换后的梯度轮廓生成HR图像的目标梯度场，将其作为HR图像重建模型中的先验图像添加。进行了广泛的实验以评估该算法在主观视觉效果，客观质量和计算时间上的价值。实验结果表明，与最新技术相比，该方法可以生成具有更好视觉质量，更低重建误差和可接受的计算效率的优质HR图像。

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