Abstract
Sparse representations are widely used tools in image super-resolution (SR) tasks. In the sparsity-based SR methods, linear sparse representations are often used for image description. However, the non-linear data distributions in images might not be well represented by linear sparse models. Moreover, many sparsity-based SR methods require the image patch self-similarity assumption; however, the assumption may not always hold. In this paper, we propose a novel method for single image super-resolution (SISR). Unlike most prior sparsity-based SR methods, the proposed method uses non-linear sparse representation to enhance the description of the non-linear information in images, and the proposed framework does not need to assume the self-similarity of image patches. Based on the minimum reconstruction errors, support vector regression (SVR) is applied for predicting the SR image. The proposed method was evaluated on various benchmark images, and promising results were obtained.
Highlights
Single image super-resolution (SISR) techniques try to enhance the resolution of an image with low resolution (LR) to obtain an image with high resolution (HR)
It can be noted that the proposed method employs the same scheme in [27,28,30], in which the ‘low-frequency’ and ‘high-frequency’ image patches are used for learning, and support vector regression is used for image refinement
The improvements of PSNR brought by our method indicate that using a non-linear sparse representation can obtain better image patch description
Summary
Single image super-resolution (SISR) techniques try to enhance the resolution of an image with low resolution (LR) to obtain an image with high resolution (HR). The non-negative sparse representation has been successfully applied in many computer vision applications, such as face recognition [20,22], motion extraction [22,23], image classification and retrieval [24,25] These non-negative sparse representations are all based on linear learning models. The training image patches must be carefully selected, which means that their methods only work for images that have similar statistical nature Motivated by this drawback of the existing sparse representation models, in this paper, we propose using a non-linear sparse representation model for single image super-resolution. Different from the existing sparsity-based methods, in this paper, a novel non-negative and non-linear sparse representation model is proposed and used for describing the image patches.
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