An Integrated Double Hybrid Fusion Approach for Image Smoothing

Anchal Kumawat, S. Panda
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引用次数: 1

Abstract

Often in practice, during the process of image acquisition, the acquired image gets degraded due to various factors like noise, motion blur, mis-focus of a camera, atmospheric turbulence, etc. resulting in the image unsuitable for further analysis or processing. To improve the quality of these degraded images, a double hybrid restoration filter is proposed on the two same sets of input images and the output images are fused to get a unified filter in combination with the concept of image fusion. First image set is processed by applying deconvolution using Wiener Filter (DWF) twice and decomposing the output image using Discrete Wavelet Transform (DWT). Similarly, second image set is also processed simultaneously by applying Deconvolution using Lucy–Richardson Filter (DLR) twice followed by the above procedure. The proposed filter gives a better performance as compared to DWF and DLR filters in case of both blurry as well as noisy images. The proposed filter is compared with some standard deconvolution algorithms and also some state-of-the-art restoration filters with the help of seven image quality assessment parameters. Simulation results prove the success of the proposed algorithm and at the same time, visual and quantitative results are very impressive.
一种集成的双混合融合图像平滑方法
在实际应用中,在图像采集过程中,由于噪声、运动模糊、相机失焦、大气湍流等各种因素的影响,采集到的图像往往会出现降级,导致图像不适合进一步分析或处理。为了提高退化图像的质量,结合图像融合的概念,对两组相同的输入图像提出了双混合恢复滤波器,并对输出图像进行融合,得到一个统一的滤波器。首先对图像集进行两次维纳滤波(Wiener Filter, DWF)反卷积处理,然后对输出图像进行离散小波变换(Discrete Wavelet Transform, DWT)分解。同样,第二图像集也同时处理,使用Lucy-Richardson Filter (DLR)进行两次反卷积,然后按照上述步骤进行处理。与DWF和DLR滤波器相比,所提出的滤波器在模糊和噪声图像的情况下都具有更好的性能。利用7个图像质量评价参数,将该滤波器与一些标准的反卷积算法和一些最先进的恢复滤波器进行了比较。仿真结果证明了所提算法的成功,同时,可视化和定量结果都令人印象深刻。
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