Research On Reconstruction Of Remote Sensing Images With Variational Model

Remote sensing is an important means to obtain the information of the terrain,which has been widely used in the field of resource census,environmental monitoring,disaster assessment,urban planning and military reconnaissance.However,remote sensing images inevitably suffer from quality degradation in the process of imaging,transmission and storage,and directly reduce the accuracy of the interpretation and information extraction,which are affected by the factors such as image system and complex working environment of remote sensing cameras.Thus,in order to improve the quality of degraded remote sensing images,it is necessary to study effective reconstruction algorithms.Here,we focus on restoring the remote sensing images suffering from the random noise,the stripe noise and haze.For the random noise,the classical ROF model and other variational models restore the images at a single fixed scale,which fail to maintain textures and other details of the information,and often introduce artifacts.To solve this problem,a random noise removal method based on multi-scale variational model is proposed.With the multi-scale variational model,the noisy image is decomposed into different scales to extract the textures and noise information of the image at different scales.In this way,the noise and image useful information are separated.Experiments show that the proposed method can effectively suppress the random noise in remote sensing images while preserving the details of the texture and the edges of the images.Remote sensing imaging systems with multi CCD(Charge-coupled Device)mosaicing often suffer from stripe noise,resulting from the presence of nonuniformity noise.Based on the analysis of the main sources of the noise and the degradation model,a multi-scale variational model is proposed for stripe removal.The energy function is constructed by combining the characteristics of stripe noise and the method of multiscale decomposition.Then,the energy function is minimized in each scale with the fixed point Gauss-Seidel iteration method.Last,the denoised image is obtained by accumulating the scale component and the detail component.Experimental results show that the proposed method can completely remove the stripe noise and introduces low distortion,compared with the typical destriping methods,for both the periodic stripe noise and the random stripe noise.For the stripe noise removal method based on the wavelet transform,discrete wavelet transform(DWT)is translation-variant,which will introduce the pseudo-Gibbs phenomenon in the destriped images if the coefficients are modified.To solve this problem,a destriping method based on the stationary wavelet transform(SWT)and unidirectional variational is proposed.Firstly,the noisy image is decomposed into one low frequency component and three high frequency components by using the SWT.Since the stripe noise is unidirectional,the stripe noise component is only included in the low frequency component and the corresponding high frequency components.In this way,the stripe noise and image useful information are separated effectively.Then,only the wavelet coefficients containing the stripe noise information are processed by the unidirectional variation method,and the other wavelet coefficients are left unprocessed.Experiments show that the method can preserve the detail information such as the edges of the images,and are free of the pseudo-Gibbs phenomenon.In hazy weather,the media transmittance is reduced exponentially with the distance because of the scattering of media.If restoration with direct inverse method,the noise will be amplified infinitely for the distant scene,where the media transmittance is close to zero.Aiming at this problem,an adaptive image dehazing algorithm based on variation is proposed.In the variational model,a spatial factor automatically adapts to the regulation intensities as the atmospheric transmittance changes: a large regulation intensity is used for distant scenes,and a small regulation intensity is used for nearby scenes.Experiments show that the proposed method can improve the visibility of the images,without noise amplification and color distortion.