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Image Super Resolution Technique based on Adaptive Lifting and Stationary Wavelet Decomposition

机译:基于自适应提升和平稳小波分解的图像超分辨率技术

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A novel algorithm for image superresolution based on the interpolation of high frequency subbands with adaptive lifting wavelet transform (ALWT) and stationary wavelet transform (SWT) is introduced. ALWT is used in order to decompose an input image into different subbands. SWT is introduced to correct the estimated coefficients. The edges are enhanced by the increment of the high frequency subbands obtained by ALWT and SWT. Afterwards, all these images are combined using ILWT to generate a super resolved image. In order to evaluate the image quality, MSE and PSNR are both computed for the resolved image. Quantitative and qualitative experiments for the proposed method showed great superiority in obtaining high resolution images with high PSNR and much better visual quality.
机译:提出了一种基于高频子带内插的自适应提升小波变换(ALWT)和平稳小波变换(SWT)的图像超分辨率新算法。为了将输入图像分解为不同的子带,使用了ALWT。引入SWT来校正估计的系数。通过ALWT和SWT获得的高频子带的增加来增强边缘。然后,所有这些图像都使用ILWT合并以生成超分辨图像。为了评估图像质量,均针对解析后的图像计算了MSE和PSNR。该方法的定量和定性实验显示出在获得具有高PSNR和更好的视觉质量的高分辨率图像方面的巨大优势。

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