Optical lenses are only able to focus a single scene plane onto the sensor, leaving the remainder of the scene subject to varying levels of defocus. The apparent depth of field can be extended by capturing a sequence with varying focal planes that is merged by selecting, for each pixel in the target image, the most focused corresponding pixel from the stack. This process is heavily dependent on capturing a stabilised sequence-a requirement that is impractical for hand-held cameras. Here we have developed a novel method that can merge a focus stack captured by a hand-held camera despite changes in shooting position and focus. Our approach is able to register the sequence using affine transformation before fusing the focus stack. We have developed a merging process that is able to identify the focused pixels for each pixel in the stack and therefore select the most appropriate pixels for the synthetically focused image. We have proposed a novel approach for capturing qualified focus stack on mobile phone cameras. Furthermore, we test our approach on a mobile phone platform that can automatically capture a focus stack as easily as a photographer capturing a conventional image.

We have developed a new face hallucination framework termed from local pixel structure to global image super-resolution (LPS-GIS). Based on the assumption that two similar face images should have similar local pixel structures, the new framework first uses the input low-resolution (LR) face image to search a face database for similar example high-resolution (HR) faces in order to learn the local pixel structures for the target HR face. It then uses the input LR face and the learned pixel structures as priors to estimate the target HR face. We present a three-step implementation procedure for the framework. Step 1 searches the database for K example faces that are the most similar to the input, and then warps the K example images to the input using optical flow. Step 2 uses the warped HR version of the K example faces to learn the local pixel structures for the target HR face. An effective method for learning local pixel structures from an individual face, and an adaptive procedure for fusing the local pixel structures of different example faces to reduce the influence of warping errors, have been developed. Step 3 estimates the target HR face by solving a constrained optimization problem by means of an iterative procedure. Experimental results show that our new method can provide good performances for face hallucination, both in terms of reconstruction error and visual quality; and that it is competitive with existing state-of-the-art methods.

In this paper, we present a novel learning-based face hallucination method based on the assumption that similar faces will have similar local pixel structures. We use the low- resolution (LR) input face to search a database for K example faces that are the most similar to the input and align them with the input accordingly. The local pixel structures of the target high-resolution (HR) image are learned from those warped HR example faces in a neighbor embedding manner, and a total variation (TV) constraint is employed to aid the learning of all pixels' embedding weights. The learned local pixel structures are then used as constraints to reconstruct a HR version of the input face. Experimental results show that the method performs well in terms of both reconstruction error and visual quality.

In this paper, we propose a region-based eigentransformation method for human face hallucination. The algorithm first uses the snake method to locate the targeted regions with high-frequency features in an edge-smoothed face mask, and then employs the eigentransformation to reconstruct the corresponding high-resolution counterpart. The resulting high-resolution face can be obtained by combining all of the reconstructed regions. As the boundaries of the respective high-frequency regions are also smooth, so the merging process at the boundaries will not produce visual artifacts. In addition, by segmenting the facial features into different regions, the eigentransformation can achieve a better performance level in reconstructing the high-frequency information. Experiments show that our proposed algorithm outperforms the original eigentransformation for face hallucination.

Gabor wavelets (GWs) have been commonly used for extracting local features for various applications, such as recognition, tracking, and edge detection. However, extracting the Gabor features is computationally intensive, so the features may be impractical for real-time applications. In this paper, we propose a set of simplified version of GWs (SGWs) and an efficient algorithm for extracting the features for edge detection. Experimental results show that our SGW-based edge-detection algorithm can achieve a similar performance level to that using GWs, while the runtime required for feature extraction using SGWs is faster than that with GWs with the use of the fast Fourier transform. When compared to the Canny and other conventional edge-detection methods, our proposed method can achieve a better performance in the terms of detection accuracy and computational complexity.