The low resolved satellite images caused by serious degradation in remote sensing weaken its utilities in practice. An effective algorithm of high resolution remote sensing image reconstruction is proposed to recover the degraded images using a precise estimated modulated transfer function (MTF) of the imaging system from a curve knife edge. The curve edge is chosen automatically and robustly among many candidate edges, which can provide a higher precision in comparison to straight edge. To suppress the artifacts and noise, the total variation (TV) method is applied as well. The experiments show this algorithm is suitable to recover a high-resolved image with a high signal-to-noise ratio (SNR).
The Point Spread Function (PSF) is one of the key indicators characterizing the signal transfer characteristics of an imaging system. Edge method is applicable to calculate the PSF of the remote sensing imaging systems for its easy implement and robust noise-resistant ability. In this paper, a Double-Knife-Edge method is proposed to recover the degraded images using a precise estimated PSF of the imaging system. The exact motion-blur direction is estimated by image differentiation firstly. Two orthogonal edges, one of which is in the same direction as the main motion-blur, are picked up from the candidate edges via Hough transform and employed to obtain edge spread functions (ESF). Derived from these ESFs, a more accurate PSF is used to deconvolute the degraded image by an image restoration algorithm based on total variation (TV) deconvolution which is capable of suppressing the artifacts and noise. The experiment results show that this algorithm is adaptive and efficient to reconstruct remote sensing images, and the reconstructed image has better PSNR, MSE and MTF than the original degraded image.
Satellite imagery always has low-resolution causing poor application in practice because the serious degradation in imaging is resulted in many factors such as atmospheric turbulence, cloud, and aberration of optical system. To reconstruct the degraded remote sensing images with a high quality, we designed an algorithm to estimate the system modulation transfer function (MTF) accurately. Phase congruency is employed to detect the edges and corners of the image first, then the significant edges, which are utilized to estimate the edge spread function (ESF) using inclined edge method, are picked up from above features through a certain line detection measurement. An image restoration algorithm based on total variation (TV) is introduced to deconvolute the degraded image with the estimated MTF which is derived from the ESF. The experiments show that this method is adaptive and efficient to recover the remote sensing images taken from a Chinese Satellite. The restored images with a higher resolution and higher signal-to-noise ratio (SNR) will improve the applications greatly.