Super Resolution Imaging Needs Better Registration for Better Quality Results

In this paper, trade-off between effect of registration error and number of images used in the process of super resolution image reconstruction is studied. Super Resolution image reconstruction is three phase process, of which registration is of at most importance. Super resolution image reconstruction uses set of low resolution images to reconstruct high resolution image during registration. The study demonstrates the effects of registration error and benefit of more number of low resolution images on the quality of reconstructed image. Study reveals that the registration error degrades the reconstructed image and without better registration methodology, a better super resolution method is still not of any use. It is noticed that without further improvement in the registration technique, not much improvement can be achieved by increasing number of input low resolution images

Optimization Transfer Approach to Joint Registration / Reconstruction for Motion-Compensated Image Reconstruction

Motion artifacts in image reconstruction problems can be reduced by performing image motion estimation and image reconstruction jointly using a penalized-likelihood cost function. However, updating the motion parameters by conventional gradient-based iterations can be computationally demanding due to the system model required in inverse problems. This paper describes an optimization transfer approach that leads to minimization steps for the motion parameters that have comparable complexity to those needed in image registration problems. This approach can simplify the implementation of motion-compensated image reconstruction (MCIR) methods when the motion parameters are estimated jointly with the reconstructed image.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85845/1/Fessler247.pd

Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution.

We demonstrate lensfree holographic microscopy on a chip to achieve approximately 0.6 microm spatial resolution corresponding to a numerical aperture of approximately 0.5 over a large field-of-view of approximately 24 mm2. By using partially coherent illumination from a large aperture (approximately 50 microm), we acquire lower resolution lensfree in-line holograms of the objects with unit fringe magnification. For each lensfree hologram, the pixel size at the sensor chip limits the spatial resolution of the reconstructed image. To circumvent this limitation, we implement a sub-pixel shifting based super-resolution algorithm to effectively recover much higher resolution digital holograms of the objects, permitting sub-micron spatial resolution to be achieved across the entire sensor chip active area, which is also equivalent to the imaging field-of-view (24 mm2) due to unit magnification. We demonstrate the success of this pixel super-resolution approach by imaging patterned transparent substrates, blood smear samples, as well as Caenoharbditis Elegans

Lensfree super-resolution holographic microscopy using wetting films on a chip.

We investigate the use of wetting films to significantly improve the imaging performance of lensfree pixel super-resolution on-chip microscopy, achieving < 1 µm spatial resolution over a large imaging area of ~24 mm(2). Formation of an ultra-thin wetting film over the specimen effectively creates a micro-lens effect over each object, which significantly improves the signal-to-noise-ratio and therefore the resolution of our lensfree images. We validate the performance of this approach through lensfree on-chip imaging of various objects having fine morphological features (with dimensions of e.g., ≤0.5 µm) such as Escherichia coli (E. coli), human sperm, Giardia lamblia trophozoites, polystyrene micro beads as well as red blood cells. These results are especially important for the development of highly sensitive field-portable microscopic analysis tools for resource limited settings

Partition-based Interpolation for Color Filter Array Demosaicking and Super-Resolution Reconstruction

A class of partition-based interpolators that addresses a variety of image interpolation applications are proposed. The proposed interpolators first partition an image into a finite set of partitions that capture local image structures. Missing high resolution pixels are then obtained through linear operations on neighboring pixels that exploit the captured image structure. By exploiting the local image structure, the proposed algorithm produces excellent performance on both edge and uniform regions. The presented results demonstrate that partition-based interpolation yields results superior to traditional and advanced algorithms in the applications of color filter array (CFA) demosaicking and super-resolution reconstruction

Bioimage informatics in STED super-resolution microscopy

Optical microscopy is living its renaissance. The diffraction limit, although still physically true, plays a minor role in the achievable resolution in far-field fluorescence microscopy. Super-resolution techniques enable fluorescence microscopy at nearly molecular resolution. Modern (super-resolution) microscopy methods rely strongly on software. Software tools are needed all the way from data acquisition, data storage, image reconstruction, restoration and alignment, to quantitative image analysis and image visualization. These tools play a key role in all aspects of microscopy today – and their importance in the coming years is certainly going to increase, when microscopy little-by-little transitions from single cells into more complex and even living model systems. In this thesis, a series of bioimage informatics software tools are introduced for STED super-resolution microscopy. Tomographic reconstruction software, coupled with a novel image acquisition method STED< is shown to enable axial (3D) super-resolution imaging in a standard 2D-STED microscope. Software tools are introduced for STED super-resolution correlative imaging with transmission electron microscopes or atomic force microscopes. A novel method for automatically ranking image quality within microscope image datasets is introduced, and it is utilized to for example select the best images in a STED microscope image dataset.Siirretty Doriast