Research On Image Restoration Of Space Camera With Wide Field Of View

With the increasing demand for ground images with wide coverage, the spacecamera with wide field of view becomes the focus and the hot spot in the field ofspace technology. Its development costs enormous human and material resources. Itsmain products are high-quality remote sensing images with wide coverage, so theimage quality is the key to the success of the space camera with wide field of view.The space camera with wide field of view obtains ground images by multi-chipCCD mosaic and multiple imaging channels. Because of the overlapped pixelsbetween the stitching CCDs, the multi-channel images are discontinuous. There isan overlapped region between the images of adjacent channels. In the imagingprocess, many factors will result in image degradation, such as the CCDperformance, the vibration of the satellite, the defocus of the imaging system andatmospheric turbulence. Because the space camera can't attach a perfect imageprocessing system in the existing condition, the acquired multi-channel images areblurry due to degradation. In order to obtain a continuous and seamless widecoverage image which covers the entire field of view and maximize the quality ofthe wide coverage image, the image degradation of each channel needs to berecovered. At the same time, the multi-channel images need to be stitched toacquire a continuous and seamless wide coverage image.Due to the discontinuity of multi-channel images and image degradation of each channel, this paper deals with image restoration of the space camera with wide fieldof view in two aspects. First of all, the image degradation of each channel needs tobe recovered to get clearer images, making the interested targets easier to identify.Secondly, the multi-channel images whose clarity is enhanced need to be stitchedautomatically to get a high quality continuous and seamless wide coverage image.The data amount of the remote sensing images dealt in this paper is huge. Therefore,the time overhead of the restoration algorithms is a key issue to consider in thecondition that the enhancement of image quality is ensured.Restoration on image degradation is the focus of this paper. The degradationreasons of the remote sensing images are analyzed detailedly. Image degradation andrestoration models are built. The recovered process is divided into denoising anddeconvolution. In the denoising step, the improved notch filtering method withsingular point detection and wavelet method with adaptive soft threshold areproposed. The two methods not only remove almost all the strip noise in the remotesensing images but also retain the edge information well. The core of thedeconvolution step is estimation of the on-orbit point spread function. Theknife-edge method is adopted to estimate the on-orbit point spread function of theimaging system. In order to ensure the accuracy of the point spread functionestimation, the Fermi fitting is done to the edge spread function. Due to the wellperformance and small time overhead, the Wiener filtering algorithm is used torestore the degraded images with the estimated point spread function as a parameter.In order to overcome the parasitic ripple and ringing phenomenon caused by thetraditional Wiener filtering, the optimal window and edge detection are combinedwith the Wiener filtering to restore images. The clarity of the degraded imagesprocessed by this method is significantly improved. After the remote sensing imagewith complex terrain is recovered, its gray mean grads (GMG) increases form4.775to11.333, and its Laplacian Sum (LS) increases form18.676to58.493.After image degradation of each channel is recovered, the clarity is improvedand the features are easier to extract. All this are benefit to the continuous and seamless automatic mosaic of the multi-channel images. The automatic mosaic isdivided into image registration and image fusion. The template matching algorithmwith optimal steps is proposed as image registration algorithm. The execution timeof this algorithm is approximately0.85%of the traditional template matchingalgorithm and1.3%of SSDA algorithm. It is a fast image registration algorithmwhich is suitable for the space camera with wide field of view. The fade-in andfade-out method is used to complete image fusion. It is a simple and fast algorithmwith wonderful effect in eliminating the stitching seams. It is suitable for the remotesensing images with a huge amount of data.