A Log-WT Based Super-resolution Algorithm
基于Log-WT的人脸图像超分辨率重建

Most learning-based super-resolution algorithms neglect the illumination problem. In this paper, a new image representation called Logarithmic-Wavelet Transform (Log-WT) is developed for the elimination of the lighting effect in the image. Meanwhile, a Log-WT based method is proposed to combine super-resolution and shadow removing into a single operation. In this method first intrinsic, illumination invariant features of the image are extracted with exploiting logarithmic-wavelet transform. Then an initial estimation of high resolution image is obtained based on the assumption that small patches in low resolution space and patches in high resolution space share the similar local manifold structure. Finally the target high resolution image is reconstructed by applying the special face constraints in pixel domain. Experimental results demonstrate that the proposed method simultaneously achieves single-image super-resolution and image enhancement especially shadow removing. After that, reconstruction results are used for face recognition which improves the recognition rate.