Advance Neighbor Embedding for Image Super Resolution

Dr Ruikar Sachin D,Mr Wadhavane Tushar D

doi:10.24297/ijct.v8i2.3384

Abstract

This paper presents the Advance Neighbor embedding (ANE) method for image super resolution. The assumption of the neighbor-embedding (NE) algorithm for single-image super-resolution Reconstruction is that the feature spaces are locally isometric of low-resolution and high-resolution Patches. But, this is not true for Super Resolution because of one to many mappings between Low Resolution and High Resolution patches. Advance NE method minimize the problem occurred in NE using combine learning technique used to train two projection matrices simultaneously and to map the original Low Resolution and High Resolution feature spaces onto a unified feature subspace. The Reconstruction weights of k- Nearest neighbour of Low Resolution image patches is found by performing operation on those Low Resolution patches in unified feature space. Combine learning use a coupled constraint by linking the LRâ€“HR counterparts together with the k-nearest grouping patch pairs to handle a large number of samples. So, Advance neighbour embedding method gives better resolution than NE method

Highlights

The Digital imaging system has lot of limitations, the imaging environment important part of capturing an image, so it is not always easy to capture an image at a desired high-resolution (HR) level
To address this type of problem, the existing variations of the NE algorithm for image Super Resolution mainly concentrate on two aspects: one is to select more suitable features to characterize Low Resolution image patches such that the neighborhood relationship between LR–HR patch pairs can be preserved as consistently as possible and the other is to build a better reconstruction function by imposing some consistency constraints on HR–LR pairs
Thereafter, for each LR image patch to be super-resolved, its nearest grouping patch pairs (GPPs) is searched to perform combine learning for the unified feature subspace

Summary

INTRODUCTION

The Digital imaging system has lot of limitations, the imaging environment important part of capturing an image, so it is not always easy to capture an image at a desired high-resolution (HR) level. Neighbor Embedding for SR reconstruction [19] is promising, except for its limitation of a locally isometric assumption in the Low Resolution and High Resolution feature spaces To address this type of problem, the existing variations of the NE algorithm for image Super Resolution mainly concentrate on two aspects: one is to select more suitable features to characterize Low Resolution image patches such that the neighborhood relationship between LR–HR patch pairs can be preserved as consistently as possible and the other is to build a better reconstruction function by imposing some consistency constraints on HR–LR pairs. Thereafter, for each LR image patch to be super-resolved, its nearest GPPs (including k-NNs of LR–HR image patch pairs) is searched to perform combine learning for the unified feature subspace. The optimal reconstruction weights for SR reconstruction are estimated in the unified feature subspace rather than solely in the LR feature space

MAP Reconstruction Framework

Combine Learning on Patch Pairs

CONCLUSION