SINGLE IMAGE SUPER-RESOLUTION BASED ON GRADIENT PROFILE SHARPNESS
0 likes255 views
1) Single image super resolution aims to generate a high-resolution image from a low-resolution input image using image processing techniques. 2) The paper proposes a novel super resolution algorithm based on gradient profile sharpness (GPS), which is a metric that measures edge sharpness. 3) GPS is extracted from two gradient description models and the relationship between GPS in different resolutions is estimated statistically. This estimated relationship is then used as a prior in reconstructing the high-resolution image.
SINGLE IMAGE SUPER-RESOLUTION BASED ON GRADIENT PROFILE SHARPNESS
- 1. SINGLE IMAGE SUPER-RESOLUTION BASED ON GRADIENT PROFILE SHARPNESS ABSTRACT Single image super resolution 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 super resolution 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.