结合稀疏表示与匹配梯度分布的图像复原

针对基于稀疏表示的传统图像复原方法无法准确恢 复图像小尺度细节的不足,提出了一种结合稀疏 表示与匹配梯度分布的图像复原方法。首先在稀疏表示图像复原模型的基础上,利用参数化 的超拉 普拉斯分布估计原始图像的梯度分布；然后,通过对图像的梯度分布进行全局约束,利用梯 度直方图匹配 操作匹配图像梯度分布,使复原图像的梯度分布尽可能接近原始图像。仿真实验结果表明 , 本文算法能够取得较优的复原效果, 并且能以较高精度复原图像的细节信息。

Image restoration combining sparse representation and matching gradient distribution

An image o ften contains different levels of degradation.In order to obtain a higher qualit y image from the degraded image,different kinds of restoration methods have been proposed.Since the sparse characteristics of natural images ha ve been well revealed in the past several decades,the sparse representation based methods ar e considered as the most promising algorithms.However,the present image restoration methods base d on sparse representation cannot accurately represent small scale details of reconstructed images.To overcome this drawback,a new image restoration method which combines sparse rep resentation and matching gradient distribution is proposed.To improve the performance of th e traditional image restoration model based on sparse representation,the proposed algorithm u tilizes a parameterized hyper-Laplace model to estimate the gradient distribution of the original image.Then a global constraint is applied on the gradient distribution of images,and the h istogram specification operation is performed to match the gradient distribution.Thus th e gradient distribution of the reconstructed image is similar to that of the original image .Numerical experimental results indicate that the proposed algorithm has good recovery performance,an d can represent the image details with high accuracy.