Learning-based super resolution using kernel partial least squares

In this paper, we propose a learning-based super resolution approach consisting of two steps. The first step uses the kernel partial least squares (KPLS) method to implement the regression between the low-resolution (LR) and high-resolution (HR) images in the training set. With the built KPLS regression model, a primitive super-resolved image can be obtained. However, this primitive HR image loses some detailed information and does not guarantee the compatibility with the LR one. Therefore, the second step compensates the primitive HR image with a residual HR image, which is the subtraction of the original and primitive HR images. Similarly, the residual LR image is obtained from the down-sampled version of the primitive HR and original LR image. The relation of the residual LR and HR images is again modeled with KPLS. Integration of the primitive and the residual HR image will achieve the final super-resolved image. The experiments with face, vehicle plate, and natural scene images demonstrate the effectiveness of the proposed approach in terms of visual quality and selected image quality metrics.

Graphical abstractFigure optionsDownload full-size imageDownload high-quality image (72 K)Download as PowerPoint slideResearch Highlights► Kernel partial least squares (KPLS) method is used for image super resolution. ► The relationship between high- and low-resolution residual images is established to achieve an improved result. ► A two-step learning-based procedure is implemented. ► The effectiveness of the proposed method is demonstrated with experimental results.