Single Image Super-Resolution Based on Gradient Profile Sharpness
Abstract
In this paper, a novel image super-resolution algorithm is proposed based on GPS (Gradient Profile Sharpness). 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 (high resolution) 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 to state-of-the-art works . Single Image Super-Resolution Based on Gradient Profile Sharpness
HARDWARE REQUIREMENT:
- Speed – 1 GHz
- Processor – Pentium –IV
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA
SOFTWARE REQUIREMENTS:
- Operating System : Windows XP or Win7
- Front End : Microsoft Visual Studio .NET 2008
- Script : C# Script
- Back End : MS-SQL Server 2005
- Document : MS-Office 2007
EXISTING SYSTEM:
Single image super-resolution is a classic and active image processing problem, which aims to generate a high resolution 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
PROPOSED SYSTEM:
- More sophisticated interpolation models have also been proposed
- To reduce the dependence on the training HR image, self-example based approaches were proposed, which utilized the observation that patches tended to redundantly recur inside an image within the same image scale as well as across different scales or there existed a transformation relationship across image space
- . These approaches are more robust, however there are always some artifacts on their super-resolution results. Generally, the computational complexity of learning-based super-resolution approaches is quite high.
- Various regularization terms have been proposed based on local gradient enhancement and globalgradient sparsity . Recently, metrics of edge sharpness have attracted researchers attention as the regularization term, since edges are of primary importance invisual image quality .
- Based on the transformed GPS, two gradient profile transformation models are proposed, which can well keep profile shape and profile gradient magnitude sum consistent during the profile transformation.
- Finally, the target gradient field of HR (high resolution) image is generated from transformed gradient profiles, which is added as the image priors in HR image reconstruction model.