Research On Reconstruction Based Super-Resolution Technique

In most electronic imaging applications, images with high resolution (HR) are desired and often required. A HR image can offer more details that may be critical in various applications, such as medial diagnosis, satellite imaging, and video surveillance so on. However, the current resolution level of CCD and CMOS image sensors can not make a captured image have no visible artifacts when it is magnified. It is expensive and difficult to increase the current resolution level by improving hardware performance, which is not considered effective.Super-resolution technique, a kind of image fusion, can use signal processing techniques to obtain an HR image (or sequence) from observed multiple low-resolution (LR) images of the same scene, and overcome these limitations of the sensors and optics manufacturing technology. Recently, such a resolution enhancement approach has been one of the most active research areasThis dissertation introduces mathematic model of LR imaging, reviews reconstruction-based and learning-based SR algorithms, and carries on analysis and research. Finally some significant schemes are proposed. In general, the main ideas are included as follows:1. Edge-projection based image registration scheme is proposed. In this scheme, firstly, Canny operator is used to extract the edges of reference image and sensed image, since it can detect strong and poor edges at the same time, and represents good performance, such as anti-noise, high precision of edge orientation and low ratio of error detection. Then, Radon transform can exactly reflect edge orientation, and has two useful properties: shift-invariant and rotation-invariant, so it is used to project the extracted edges along different angles. Next, cross-correlation-based approach is used to estimate global rotation and shift. Finally, data fit is used to improve the accuracy of image registration. Registration precision is dependent on fine resample distance. 2. Regularization SR reconstruction considering inaccurate image registration is proposed.a. Since the performance of the registration algorithms cannot ensure estimated motion parameters coincident with original ones in certain environments, displaced frame differential (DFD) caused by an inaccurate registration should be considered in the reconstruction procedure. The error caused by the DFD, which is called cross-channel registration error, is considered as mean value and combined with observed within-channel AWGN to construct a new Gaussian noise..b. Since registration error is pixel-wise different, two kinds of constraints are regulated according to the idea of Miller's regularization. Regularization parameters connect the two constraints to construct regularized cost function. In term of contamination degree of within-channel AWGN to LR image and cross-channel registration error, regularization parameters are adaptively selected to balance the fidelity of desired HR image to LR images and its smoothness.c. Two kinds of recursion implementation schemes are proposed to reconstruct a desired HR image, that is, synchronous recursion and parallel recursion.d. The smoothness and ideal sampling operation of limited sensor array to HR image are respectively implemented. In reconstruction procedure, the smoothness is removed as well as optical blur. Up-sampling operation is implemented by nearest interpolation to restrain edge artifact.3. Error-parameter analysis based SR blind identification is proposed. Blur operator can be represented by parameter model, so the identification of blur operator is converted to parameter identification.a. In single channel parameter identification, error-parameter curve is obtained by error-parameter analysis, and blur parameter is chosen experimentally in term of curve tendency.b. In multi-channel single-parameter identification, error-parameter analysis is combined with searching method due to alternate influence of reconstruction error between multiple channels. Minimal reconstruction error rule is used to identify blur parameter. The combination with searching method reduces computational cost and makes speedy parameter identification come true.4. Independent Component Analysis (ICA) based SR face hallucination and recognition is proposed. In the scheme, training HR face samples compose mixed data matrix. FastICA algorithm is used to separate the mixed data for obtaining independent HR source face images. Then the HR source face image is down-sampled to obtain approximately independent LR source face images. An input LR face image is projected in the space formed by the LR source face images, and unconstrained least square method is used to obtain projection coefficients. The projection coefficients are retained and linearly mix the corresponding HR source face images. Finally HR face hallucination and recognition are performed.In sum, three aspects about reconstruction based SR algorithm, such as image registration, regularization which makes ill-posed SR problem well-posed, and blur identification, are investigated in this dissertation, and ICA is used in learning based SR algorithm to realize HR face hallucination and recognition. Finally, the problem to be solved related to this research field and future research topics are summarized, furthermore, the prospect of the developing tendency is analyzed as well.