Continuous digital zooming using local self-similarity-based super-resolution for an asymmetric dual camera system.

This paper presents a digital zooming method using a super-resolution (SR) algorithm based on the local self-similarity between the wide- and tele-view images acquired by an asymmetric dual camera system. The proposed SR algorithm consists of four steps: (i) registration of an optically zoomed image to the wide-view image, (ii) restoration of the central region of the zoomed wide-view image, (iii) restoration of the boundary region of the zoomed wide-view image, and (iv) fusion of the results from steps (ii) and (iii). Since an asymmetric dual camera system acquires different-resolution images on the same scene due to the different optical specifications, the proposed method can restore the low-resolution wide-view image using the ideal high-frequency component estimated from the optically zoomed image. Experimental results demonstrate that the proposed method can provide significantly improved high-resolution wide-view images compared to existing single-image-based SR methods.

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images.

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures.

Single image super-resolution using locally adaptive multiple linear regression.

This paper presents a regularized superresolution (SR) reconstruction method using locally adaptive multiple linear regression to overcome the limitation of spatial resolution of digital images. In order to make the SR problem better-posed, the proposed method incorporates the locally adaptive multiple linear regression into the regularization process as a local prior. The local regularization prior assumes that the target high-resolution (HR) pixel is generated by a linear combination of similar pixels in differently scaled patches and optimum weight parameters. In addition, we adapt a modified version of the nonlocal means filter as a smoothness prior to utilize the patch redundancy. Experimental results show that the proposed algorithm better restores HR images than existing state-of-the-art methods in the sense of the most objective measures in the literature.