Content-Aware Enhancement of Images With Filamentous Structures.

In this paper, we describe a novel enhancement method for images containing filamentous structures. Our method combines a gradient sparsity constraint with a filamentous structure constraint for the effective removal of clutter and noise from the background. The method is applied and evaluated on three types of data: 1) confocal microscopy images of neurons; 2) calcium imaging data; and 3) images of road pavement. We found that the images enhanced by our method preserve both the structure and the intensity details of the original object. In the case of neuron microscopy, we find that the neurons enhanced by our method are better correlated with the original structure intensities than the neurons enhanced by well-known vessel enhancement methods. Experiments on simulated calcium imaging data indicate that both the number of detected neurons and the accuracy of the derived calcium activity are improved. Applying our method to real calcium data, more regions exhibiting calcium activity in the full field of view were found. In road pavement crack detection, smaller or milder cracks were detected after using our enhancement method.

Structure-Based Intensity Propagation for 3-D Brain Reconstruction With Multilayer Section Microscopy.

Microscopy is widely used for brain research because of its high resolution and ability to stain for many different biomarkers. Since whole brains are usually sectioned for tissue staining and imaging, reconstruction of 3D brain volumes from these sections is important for visualization and analysis. Recently developed tissue clearing techniques and advanced confocal microscopy enable multilayer sections to be imaged without compromising the resolution. However, noticeable structure inconsistence occurs if surface layers are used to align these sections. In this paper, a structure-based intensity propagation method is designed for the robust representation of multilayer sections. The 3D structures in reconstructed brains are more consistent using the proposed methods. Experiments are conducted on 367 multilayer sections from 20 mouse brains. The average reconstruction quality measured by the structure consistence index increases by 45% with the tissue flattening method and 29% further with the structure-based intensity propagation.

Comparison-Based Image Quality Assessment for Selecting Image Restoration Parameters.

Image quality assessment (IQA) is traditionally classified into full-reference (FR) IQA, reduced-reference (RR) IQA, and no-reference (NR) IQA according to the amount of information required from the original image. Although NR-IQA and RR-IQA are widely used in practical applications, room for improvement still remains because of the lack of the reference image. Inspired by the fact that in many applications, such as parameter selection for image restoration algorithms, a series of distorted images are available, the authors propose a novel comparison-based IQA (C-IQA) framework. The new comparison-based framework parallels FR-IQA by requiring two input images and resembles NR-IQA by not using the original image. As a result, the new comparison-based approach has more application scenarios than FR-IQA does, and takes greater advantage of the accessible information than the traditional single-input NR-IQA does. Further, C-IQA is compared with other state-of-the-art NR-IQA methods and another RR-IQA method on two widely used IQA databases. Experimental results show that C-IQA outperforms the other methods for parameter selection, and the parameter trimming framework combined with C-IQA saves the computation of iterative image reconstruction up to 80%.