Wireless sensor networks, in combination with image sensors, open up a grand sensing application field. It is a challenging problem to recover a high resolution image from its low resolution counterpart, especially for low-cost resource-constrained image sensors with limited resolution. Sparse representation-based techniques have been developed recently and increasingly to solve this ill-posed inverse problem. Most of these solutions are based on an external dictionary learned from huge image gallery, consequently needing tremendous iteration and long time to match. In this paper, we explore the self-similarity inside the image itself, and propose a new combined self-similarity superresolution solution, with low computation cost and high recover performance. In the self-similarity image super resolution model , a small size sparse dictionary is learned from the image itself by the methods such as . The most similar patch is searched and specially combined during the sparse regulation iteration. Detailed information, such as edge sharpness, is preserved more faithfully and clearly. Experiment results confirm the effectiveness and efficiency of this double self-learning method in the image super resolution. 1. Introduction Wireless sensor networks, in combination with image sensors, open up a grand sensing application field. Visual information provided by image sensor is the most intuitive information perceived by human, especially for recognition, monitoring, and surveillance. Low-cost and resource-constrained image sensors with limited resolution are mainly employed [1–3]. Recovery from low resolution to high resolution is the pressing need for image sensor node. Image super resolution receives more and more interests recently, which has lots of applications in image sensor, digital cameras, mobile phone, image enhancement, high definition TV [4–6], and so forth. It aims to reconstruct a high-resolution image from the low-resolution one based on reasonable assumptions or prior knowledge. From the view of the target image, the image can be generated after downsampling and some blurring operator. Hence, the work has always been formulated as an inverse problem: where is the image to be recovered, is the known image, is the downsampling operator, is the blurring operator that minimizes the high frequency aliasing effect, and is the noise. Traditionally, the downsampling operator and blurring operator are conducted at the same time. Hence, we can use the following formulation (2) instead of (1): where is the generalized blurring and downsampling operator.
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