Image Quality Assessment Applied To Real-time Image Restoration System On Space Camera

On-orbit image restoration of the space remote-sensing camera have been realized, however, whether the on-orbit image restoration system functions well, and whether the image quality have been elevated after recovery, it needs some methods to estimate. The quality of the restored images can directly reflect the performance of the recovery system and the validity and stability of recovery algorithms, therefore, this paper has carried out the image quality assessment for on-orbit image restoration system, and evaluate the system capability from image. Five evaluate parameters are chosen based on remote sensing system metrics and image quality assessments, which are signal-to-noise ratio, modulation transfer function, sharpness, contrast and ringing. The main research results are as follows:In the process of image restoration, it will be in a certain extent to enlarge the noise when improving the image MTF and the clarity, noise is an important parameter on image quality assessment. Estimating the noise of remote sensing image is designed based on the method of block. The image is divided into small pieces of 5*5, calculate the variance of each block, mark these blocks which variance lower than the global variance, count the average of marked blocks, The results are obtained as the image noise estimation. The accuracy and the validity of the proposed method are verified by experiments.Modulation transfer function is an important index of optical imaging system, which is a function of spatial frequency transmission. After analyzing and comparing the slanted-edge method, a new method is proposed to calculate the MTF with arbitrary shape edge image, analyzed the effect of the algorithm steps and image selection. Using the MTFC technology to reconstruct different images, the MTF value and the evaluation indexes are improved, the experimental results prove the feasibility of extracting MTF with arbitrary shape edge image, and the effectiveness of image restoration.In this paper, two kinds of clarity assessments are proposed. From the global perspective, extract the image skeleton, use the number of skeleton pixels as sharpness parameter, compare the sharpness parameters between distorted image and recovered image, calculate the sharpness upgrade rate. Through the experiments of different remote sensing images to verify the validity and reliability of image sharpness evaluation using the method, also confirmed that the method has anti-noise performance and the weak ringing ripples inhibitory effect. From the local start, the no-reference resolution based on extracting the line spread function of target edge region in arbitrary image. Compute the gradient of target region, find the maximum gradient point and its direction to draw the line spread function curve, use the full width at half maximum as the image sharpness parameter. From the definition of the homologous image, the performance of the algorithm is tested. The results show that the method can evaluate the image’s clarity, without the need of reference image and has good anti-noise performance.In the remote sensing system, the illumination is insufficient, and the dynamic range of the imaging sensor is too small will lead to low contrast. At the same time, the contrast of the image will be changed after image restoration. Summarize and compare the current contrast evaluation method, select the Weber contrast as the contrast evaluation index of this paper. Due to the high frequency cut-off in frequency domain make image appeared ringing, the less accurate of PSF estimation, the greater ringing. To obtain simple texture region edge and fringe ringing, use the the ratio between the ringing and the fringe as ringing metric.An image quality assessment toolbox is designed to facilitate the use. The toolbox has simple image preprocessing functions, it can wipe off the push-broom noise, as well as the original image corresponding to the point spread function and its recovery. Read the image before and after restoration, apply the different indexes to evaluate. The assessment results are shown in the corresponding interface.