Research On Image Quality Assessment And Improvement For Visible Remote Sensing Image

Visible remote sensing imaging plays an important role in earth observation,which is vital for urban planning,resource management,environmental monitoring and other fields.Remote sensing image is the product of remote sensing imaging,and its significance lies in identifying the target from the image and then analyzing the target.The image quality of the remote sensing image directly determines the identifiability of the target in the image.Therefore,whether the remote sensor can achieve high-quality imaging is a key factor related to the success or failure of a space remote sensing mission.In the process of remote sensing imaging link,the imaging will be degraded by lighting conditions,atmospheric environment,optical imaging systems,electronic circuit systems,and satellite platforms,causing loss of image information and degradation of image quality,which will affect subsequent image interpretation,then seriously restricting the practical application of remote sensing images in various fields.Using image processing algorithms to improve remote sensing image quality has the characteristics of low cost and high cost performance.Remote sensing images contain rich target scene information.How to restore more image details through processing algorithms to improve image quality has always been of concern to scholars focus.In addition,remote sensing images can be used as the main basis for measuring the quality of remote sensing imaging.The assessment of remote sensing image quality will not only help monitor the on-orbit imaging performance of the remote sensor,and guide the adjustment of the operating parameters of the remote sensor on orbit,but also help quantification the actual application value of remote sensing image products,therefore has important research significance.Compared with subjective assessment methods,objective image quality assessment methods are simpler and more stable.However,these methods have many bottlenecks that need to be overcome,such as insufficient accuracy of evaluation results,limited to specific target scenes,and single functions.In response to the above problems,this article focuses on the image quality assessment and improvement technology of visible light remote sensing images.The main research content is divided into four parts:1.The optical remote sensing imaging link model is analyzed.The causes of the degradation factors of each link are clarified and modeled.The process of information transfer during remote sensing imaging is modeled as MTF link and Signal-Noise link.The modulation transfer characteristics and noise characteristics of each link are analyzed,and the quantitative analysis of remote sensing imaging quality is realized.2.Aiming at the problems of universality and subjective and objective consistency,in-depth research has been carried out on the objective assessment of remote sensing image quality.The statistical characteristics of the spatial natural scene of remote sensing images are discussed,and the regularity of various statistical characteristics under image degradation is analyzed.An image feature extraction strategy based on block matching is proposed,which improves the effectiveness of image feature extraction,and then an image quality characterization model based on the NSS(Natural Scene Statistics)characteristics is established.On this basis,the image quality reference model is established based on a large number of "ideal natural" remote sensing images,and the distance measurement between the model parameters of the image block and the parameters of the reference model is proposed as the estimation of the local image quality.The weighted average of all local quality scores can realize the final image quality assessment.This method has good consistency with the subjective image quality assessment,and can realize the universal assessment of image quality in different remote sensing scene.3.Carried out the imaging performance evaluation research of remote sensors based on remote sensing images.The consistency between the image statistical features and the imaging performance characterization is analyzed,and the effectiveness of various statistical features in the description of imaging quality is verified.Furthermore,the consistency between the no-reference image quality assessment model and different imaging performance evaluation indicators is analyzed,and it is verified that the no-reference image quality evaluation model is effective in the imaging performance characterization and evaluation of remote sensors,and can be used for imaging performance monitoring of remote sensors.4.Blind image restoration technology of a single noisy fuzzy remote sensing image is studied.In view of the lack of prior information in practical applications and insufficient detail recovery capabilities of existing algorithms,we innovatively propose to use the sparsity characteristics of images high gradients to construct a hybrid gradient sparse prior constraint model based on low and high gradients of images.At the same time,an adaptive adjustment strategy based on image entropy is introduced to optimize the constraint ratio of the two gradient priors to achieve better convergence.Experiments show that this method can produce richer image details and sharper image edges,thereby effectively improving image quality.