Development of robust neighbor embedding based super-resolution scheme Deepasikha Mishra n , Banshidhar Majhi, Pankaj Kumar Sa, Ratnakar Dash Pattern Recognition Research Laboratory, Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Odisha 769008, India article info Article history: Received 3 August 2015 Received in revised form 3 March 2016 Accepted 16 April 2016 Communicated by Jiwen Lu Available online 6 May 2016 Keywords: Super-resolution Histogram matching Global neighborhood selection Locally linear embedding Robust principal component analysis Robust locally linear embedding abstract In this paper, we propose a robust neighbor embedding super-resolution (RNESR) scheme to generate a super-resolution (SR) image from a single low-resolution (LR) image. It utilizes histogram matching for selection of best training pair of images. This helps to learn co-occurrence prior to high-resolution (HR) image reconstruction. The global neighborhood size is computed from local neighborhood size, which avoids the over-fitting and under-fitting problem during neighbor embedding. Robust locally linear embedding (RLLE) is used in place of locally linear embedding (LLE) to generate HR image. To validate the scheme, exhaustive simulation has been carried out on standard images. Comparative analysis with respect to different measures like peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) reveals that the RNESR scheme generates high-quality SR image from a LR image as compared to existing schemes. & 2016 Elsevier B.V. All rights reserved. 1. Introduction Image analysis is an important direction of research in the field of image processing and computer vision. Image resolution plays a significant role during image analysis. The higher the resolution of an image, the more accurate is its analysis. However, during image acquisition due to some unfavorable conditions we get a low- resolution (LR) image with loss of information. Hence, achieving high-resolution (HR) image from a low-resolution image becomes a necessity. To our favor, there exists a technique called super- resolution (SR) to achieve HR images from corresponding LR ones. SR uses one or multiple LR images to produce a HR image with high spatial resolution. Due to loss of missing frequency compo- nents, there is a possibility of information loss in a SR image during the process of conversion. Hence, the major challenge in SR process is to enhance the quality of the LR image by preserving the missing high-frequency components. The primary task of SR pro- cess is to produce an image having alias free, up-sampled, and high spatial frequency from a LR image [1]. Over the past years, the SR algorithms have been extensively used in computer vision applications like remote sensing, astronomical imaging, medical imaging, and video surveillance. SR is broadly divided into two categories namely, reconstruction-based SR and recognition-based SR. Reconstruction-based SR refers to generation of HR image from a degraded LR image through traditional upscaling methods [2]. Recognition-based SR utilizes learning algorithms as it identifies pre-configured patterns hidden in LR images. Hence, recognition based SR is also known as learning based SR and it has been widely used in detection, recognition, and identification. This paper proposes a robust learning based SR algorithm to generate a HR image from a single LR image. The scheme utilizes neighbor embedding approach and suitably named as robust neighbor embedding based super-resolution (RNESR). RNESR is trained using known LR–HR image pairs to generate the infor- mation with respect to local geometry and neighborhood. Further, it uses histogram matching to select the best LR–HR image pairs for training. Subsequently, the scheme is validated using LR images selected from training pairs as well as images not used during training to generate their corresponding HR image. The proposed RNESR scheme is simulated along with other competent schemes are also simulated, and results are compared with respect to peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) [3], and feature similarity index (FSIM) [4]. It is observed that RNESR scheme outperforms others with respect to qualitative and quan- titative parameters. The rest of the paper is organized as follows. Section 2 presents the related work. The proposed RNESR algorithm is described detail in Section 3. The experimental results and discussion are Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/neucom Neurocomputing http://dx.doi.org/10.1016/j.neucom.2016.04.013 0925-2312/& 2016 Elsevier B.V. All rights reserved. n Corresponding author. E-mail addresses: deepasikhame@gmail.com, 512cs606@nitrkl.ac.in (D. Mishra), bmajhi@nitrkl.ac.in (B. Majhi), pankajksa@nitrkl.ac.in (P.K. Sa), ratnakar@nitrkl.ac.in (R. Dash). Neurocomputing 202 (2016) 49–66