Total variation restoration of the defocus image based on spectral priors

Peng Liu; Dingsheng Liu; Zhiwen Liu

doi:10.1117/12.864662

22 October 2010 Total variation restoration of the defocus image based on spectral priors

Peng Liu, Dingsheng Liu, Zhiwen Liu

Proceedings Volume 7830, Image and Signal Processing for Remote Sensing XVI; 783018 (2010) https://doi.org/10.1117/12.864662
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

In this article, we de-blur one of the out of focus image among several multispectral (MS) remote sensing images by total variation method. The no blur images are used as priors in the restoration of the out of focus image. Although the distributions of the pixel intensity of the multimodal image of different CCD sensors are greatly different form each other, the directions of their edges are very similar. Then, these similar structures and edge information are used as the important priors or constraints in the total variation image restoration. The steps are: first, the PAN (panchromatic) image is denoted approximately as the weighted sum of all the bands of MS images, and the weight parameters of the relationship between the PAN image and the MS images are computed by least square method; Second by the relationship and the weight parameters, an initial estimation of the out of focus image is calculated; third, the total variation image restoration is local linearized by fixed point iterative method; fourth, the initial estimation for the out of focus image in the third step is brought to the fixed point iteration. At last, by introduce the new priors from the relationship between MS and PAN image, the new total variation image restoration frame is constructed. The edge and gradient information from the no blur images of other channels make the total variation regularization better suppress the noise in de-convolution. The comprehensive experiments are done by using different images with different level of noise. The higher PSNR is acquired by proposed method when it is compared with some other state of art methods. Experiments confirm that the algorithm is very effective especially when the noise in blur remote sensing image is relative large.

Citation Download Citation

Peng Liu, Dingsheng Liu, and Zhiwen Liu "Total variation restoration of the defocus image based on spectral priors", Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 783018 (22 October 2010); https://doi.org/10.1117/12.864662

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