Most state-of-the-art single image blind deblurring techniques are still sensitive to image noise, leading to serious performance degradation in their blur kernel estimation when the input image noise increases. We found that reliable kernel estimation could not be given by directly using denoising and existing deblurring algorithms in many cases. We focus on how to estimate a good blur kernel from a noisy blurred image via using the image structure. First, we applied denoising as a preprocess to remove the input image noise and then computed salient image structure of the denoised result based on the total variation model. We also applied a gradient selection method to remove those salient edges that have a possible adverse effect on blur kernel estimation. Next, we adopted a two-phase estimation strategy to obtain higher quality blur kernel estimation by jointly applying kernel estimation from salient image structure and iterative support detection (ISD) kernel refinement. Finally, we used the nonblind deconvolution method based on sparse prior knowledge to restore the latent image. Extensive experiments testify to the superiority of the proposed method over state-of-the-art algorithms, both qualitatively and quantitatively.
In order to improve the accuracy of multi-spectral scene simulation, and avoid resource waste of unnecessary
computing, some researches on the radiation influence between buildings during the multi-spectral simulation in the
waveband 3-5μm and 8-12μm have been done, so as to provide theoretical support for whether it is needed to compute
the radiation influence between buildings in the multi-spectral simulation. This paper determines the primary factors that
affect the degree of radiation influence between buildings, determines the effect that the sun direct radiation to the
radiation influence between buildings, derives the computation formula for radiation influence between buildings in a
specific scene from many basic common heat radiation formula and simulates the scene radiation in multi-spectral in the
specific scene. Finally, the importance of radiation influence between buildings comparing to the entire scene simulation
radiation was evaluated based on numerical calculation. The numerical calculation results show that the radiation
influence between buildings in waveband 3-5μm can be ignored when the sun direct radiation exists, which can’t be
ignored in waveband 8-12μm. In the waveband 8-12μm, the radiation influence between nearby buildings is great in
waveband 8μm, 9μm and 10μm, more than 10% comparing to the buildings’ self radiation, which is small in waveband
11μm and 12μm, less than 4%.
Restoring blurred images is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely under constrained. Recently many single image blind deconvolution methods have been proposed, but these state-of-the-art single image deblurring techniques are still sensitive to image noise, and can degrade their performance rapidly especially when the noise level of the input blurred images increases. In this work, we estimate the blur kernel accurately by applying a series of directional low-pass filters in different orientations to the input blurred image, and effectively constructing the Radon transform of the blur kernel from each filtered image. Finally, we use a robust non-blind deconvolution method with outlier handling, which can effectively reduce ringing artifacts, to generate the final results. Our experimental results on both synthetic and real-world examples show that our method achieves comparable quality to existing approaches on blurry noisy-free images, and higher quality outputs than previous approaches on blurry and noisy images.