Face hallucination based on nonparametric Bayesian learning

© 2015 IEEE. In this paper, we propose a novel example-based face hallucination method through nonparametric Bayesian learning based on the assumption that human faces have similar local pixel structure. We cluster the low resolution (LR) face image patches by nonparametric method distance dependent Chinese Restaurant process (ddCRP) and calculate the centres of the clusters (i.e., subspaces). Then, we learn the mapping coefficients from the LR patches to high resolution (HR) patches in each subspace. Finally, the HR patches of input low resolution face image can be efficiently generated by a simple linear regression. The spatial distance constraint is employed to aid the learning of subspace centers so that every subspace will better reflect the detailed information of image patches. Experimental results show our method is efficient and promising for face hallucination

Construction of dictionaries to reconstruct high-resolution images for face recognition

This paper presents an investigation into the construction of over-complete dictionaries to use in reconstructing a super resolution image from a single input low-resolution image for face recognition at a distance. The ultimate aim is to exploit the recently developed Compressive Sensing (CS) theory to develop scalable face recognition schemes that do not require training. Here we shall demonstrate that dictionaries that satisfy the Restricted Isometry Property (RIP) used for CS can achieve face recognition accuracy levels as good as those achieved by dictionaries that are learned from face image databases using elaborate procedures

Enhancing face recognition at a distance using super resolution

The characteristics of surveillance video generally include low-resolution images and blurred images. Decreases in image resolution lead to loss of high frequency facial components, which is expected to adversely affect recognition rates. Super resolution (SR) is a technique used to generate a higher resolution image from a given low-resolution, degraded image. Dictionary based super resolution pre-processing techniques have been developed to overcome the problem of low-resolution images in face recognition. However, super resolution reconstruction process, being ill-posed, and results in visual artifacts that can be visually distracting to humans and/or affect machine feature extraction and face recognition algorithms. In this paper, we investigate the impact of two existing super-resolution methods to reconstruct a high resolution from single/multiple low-resolution images on face recognition. We propose an alternative scheme that is based on dictionaries in high frequency wavelet subbands. The performance of the proposed method will be evaluated on databases of high and low-resolution images captured under different illumination conditions and at different distances. We shall demonstrate that the proposed approach at level 3 DWT decomposition has superior performance in comparison to the other super resolution methods

Face hallucination based on sparse local-pixel structure

In this paper, we propose a face-hallucination method, namely face hallucination based on sparse local-pixel structure. In our framework, a high resolution (HR) face is estimated from a single frame low resolution (LR) face with the help of the facial dataset. Unlike many existing face-hallucination methods such as the from local-pixel structure to global image super-resolution method (LPS-GIS) and the super-resolution through neighbor embedding, where the prior models are learned by employing the least-square methods, our framework aims to shape the prior model using sparse representation. Then this learned prior model is employed to guide the reconstruction process. Experiments show that our framework is very flexible, and achieves a competitive or even superior performance in terms of both reconstruction error and visual quality. Our method still exhibits an impressive ability to generate plausible HR facial images based on their sparse local structures

Position-Patch Based Face Hallucination Using Super-Pixel Segmentation and Group Lasso

Traditional super-resolution algorithms utilized samples priors to guide image reconstruction by image-patch. All of them use square or rectangle patch for acquiring prior information. However, fixed size patches will diminish structural information obtained by patches. To make patches gain more structural information, we make two adjustments to the face hallucination: superpixel segmentation and Group Lasso. With super-pixel segmentation, we exploit structural features of human faces by segmenting face images into adaptive patches based on their appearances. Group Lasso provides additional structural information through group selection. Our experimental results show that the extra structural information attained by adjustments has a positive impact on the final reconstructed image

Super Resolution Image Reconstruction Using Linear Regression Regularized Sparse Representation

This thesis addresses the generation and reconstruction of the high resolution (HR) image by using the single low resolution (LR) image and the linear coalition of sparse coefficients from a suitably chosen over-complete dictionary.The study of compressive sensing shows that under vague conditions the sparse representation of a signal can be effectively recovered from the downsampled version of the original signal. By training both LR and HR image patches simultaneously by coupled dictionary learning, we are enforcing the similarity between the sparse representation(SR) of LR and HR image patch pairs with respective to their LR and HR dictionaries. Literature survey suggests that different extracted features are used to compute the coefficients to boost the prediction accuracy of the HR image patch reconstruction. A set of Gabor filters has been employed to extract useful features from the LR dictionary. As the super resolution is an ill posed problem, in this thesis we have considered it as an optimization problem for getting the sparsest representation of image patches using linear regression regularized with L1 norm, known as a LASSO in statistics.Our method is found to be outperforming the other previous state of art methods in both quantitative and qualitative analysis. The results reveal that proposed method shows promising results in reconstructing the image textures and edges

Recovering Variations in Facial Albedo from Low Resolution Images

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Region-Based Approach for Single Image Super-Resolution

Single image super-resolution (SR) is a technique that generates a high- resolution image from a single low-resolution image [1,2,10,11]. Single image super- resolution can be generally classified into two groups: example-based and self-similarity based SR algorithms. The performance of the example-based SR algorithm depends on the similarity between testing data and the database. Usually, a large database is needed for better performance in general. This would result in heavy computational cost. The self-similarity based SR algorithm can generate a high-resolution (HR) image with sharper edges and fewer ringing artifacts if there is sufficient recurrence within or across scales of the same image [10, 11], but it is hard to generate HR details for an image region with fine texture. Based on the limitation of each type of SR algorithm, we propose to combine these two types of algorithms. We segment each image into regions based on image content, and choose the appropriate SR algorithm to recover the HR image for each region based on the texture feature. Our experimental results show that our proposed method takes advantage of each SR algorithm and can produce natural looking results with sharp edges, while suppressing ringing artifacts. We compute PSNR to qualitatively evaluate the SR results, and our proposed method outperforms the self-similarity based or example-based SR algorithm with higher PSNR (+0.1dB)