This paper proposes a none-blind deblurring algorithm for noisy images via distributed gradient prior. The proposed image prior is motivated by observing the gradient properties of noisy images. Based on the prior of image noise's low gradient distribution, we propose an effective optimization method to deal with noisy and blurry images. In this paper, an image-gradient-related distributed factor is introduced to balance image deblurring and denoising. The distributed factor is related to image noise and works adaptively according to different noise levels of blurry images. Richardson-Lucy method is also adopted to achieve a better deconvolution result. Experiments show that our proposed method outperforms other deblurring algorithms in both preserving details and removing noise.
This paper aims to develop a novel approach of image fusion for an asymmetrical camera system when multiple images are acquired with cameras which have large differences in focal lengths but similar sensor size with an overlapping field of view. The fused image usually becomes perceptually unpleasant because the high-frequency components of a wideview image will be quite inadequate comparing to the tele-view images. Four steps are consisted in the proposed work: (i) image upscaling of the wide-view image, (ii) texture identification on the upscaled image, (iii) the performance evaluation of image upscaling, and (iv) the image inpainting for the high-frequency components of the wide-view image. The field of view of tele-view camera is set to be 4 times smaller than the wide-view camera in spatial angle in the experiment. The experiment result illustrates that the proposed algorithm brings significantly perceptual improvement to the wide-view image.
Handheld electro-optical imaging devices usually suffer from shaky problems. In this paper, we present a fast robust approach for real-time image stabilization. Since the perform1ance of image stabilization mainly depends on global motion estimation and the accuracy of motion estimation will be affected when foreground motion happened, sudden image jitters will be introduced during stabilization. To solve this problem, conventional methods detect and remove the foreground objects in motion estimation but this way works inefficiently and fails when foreground moving objects occupy large part of image. Our method is based on the following improvements: modified ORB feature points(FPs) processing, adaptive calculation of affine transformation matrix and joint utilization of two Kalman filters. It can solve the sudden image jitter problem even when there are large foreground moving objects in the image. Qualitative and quantitative evaluations demonstrate the merits of our method. Experiments show that our method solves large foreground motion problem and achieves 35 FPS for 640*480 image on Intel Core i5-4590 CPU@3.30 GHz on the windows.
In the process of remote sensing imaging, the obtained TDICCD images are always accompanied by distortion due to relative motion between imaging platform and the target. Traditional image evaluation metrics like Structural Similarity Index Measurement (SSIM) or Peak Signal to Noise Ratio (PSNR) are general assessments of image quality, but do not clearly evaluate distortion level. Considering the special properties of TDICCD images, this paper proposes a robust evaluation method to quantitatively describe motion distortion. The proposed method contains mainly three steps: image line PSF estimation, calculation of PSF deviation and overall computation of motion distortion. Numerical experiments have been done to simulate TDICCD motion distortion images under different vibration conditions whose results are later evaluated by the proposed method. Results prove that our method provides precise and robust quantitative assessment for images of different degrees of motion distortion.