Denoising is a fundamental task in image processing, aimed at estimating an unknown image from its noisy observation. In this paper, we develop a computationally simple paradigm for image denoising using superpixel grouping and principal component analysis (PCA) of similar patches within the superpixels. Our method comprises three steps. First, we perform a superpixel segmentation on the noisy images. Next, similar patches within the superpixels are grouped in order to preserve the local image structures. Finally, each group of similar patches is factorized by PCA transform and estimated by performing coefficient shrinkage in the PCA domain to remove the noise. The proposed method exploits the optimal energy compaction property of PCA on groups of similar patches in the least squares sense. The performance of our approach is experimentally verified on a variety of synthetic images at various noise levels, and on real world noisy images. Our proposed method achieves very competitive denoising performance, especially in preserving the fine image structures, compared with many existing denoising algorithms with respect to both objective measurement and visual evaluation. We also show that our proposed method is computationally more efficient than other local PCA based methods.

Nonlocal texture similarity and local intensity smoothness are both essential for solving most image inpainting problems. In this paper, we propose a novel image inpainting algorithm that is capable of reproducing the underlying textural details using a nonlocal texture measure and also smoothing pixel intensity seamlessly in order to achieve natural-looking inpainted images. For matching texture, we propose a Gaussian-weighted nonlocal texture similarity measure to obtain multiple candidate patches for each target patch. To compute the pixel intensity, we apply the -trimmed mean filter to the candidate patches to inpaint the target patch pixel-by-pixel. The proposed algorithm is compared with four current image inpainting algorithms under different scenarios, including object removal, texture synthesis, and error concealment. Experimental results show that the proposed algorithm outperforms the existing algorithms when inpainting large missing regions in images with texture and geometric structures.

Accurate detection and localization of vehicles in aerial images has a wide range of applications including urban planning, military reconnaissance, visual surveillance, and realtime traffic management. Automated detection of vehicles in aerial imagery is a challenging task, due to the density of vehicles on the road, the complexity of the surrounding environment in urban areas, and low spatial resolution of the image sensor array. We propose an automated method for detecting vehicles of varying sizes in low-resolution aerial imagery. First, we develop a new vehicle enhancement filter involving multiscale Hessian analysis. After thresholding, we refine the candidate vehicle detections based on analysis of bilateral symmetry. We show that our proposed method provides improved detection accuracy compared with existing vehicle detection algorithms for various low-resolution aerial images.

Accurate detection of individual cell nuclei in microscopy images is an essential and fundamental task for many biological studies. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. Manual detection of individual cell nuclei by visual inspection is time consuming, and prone to induce subjective bias. This makes automatic detection of cell nuclei essential for large-scale, objective studies of cell cultures. Blur, clutter, bleed-through, imaging noise and touching and partially overlapping nuclei with varying sizes and shapes make automated detection of individual cell nuclei a challenging task using image analysis. In this paper we propose a new automated method for fast and robust detection of individual cell nuclei based on their radial symmetric nature in fluorescence in-situ hybridization (FISH) images obtained via confocal microscopy. The main contributions are two-fold. 1) This work presents a more accurate cell nucleus detection system using the fast radial symmetry transform (FRST). 2) The proposed cell nucleus detection system is robust against most occlusions and variations in size and moderate shape deformations. We evaluate the performance of the proposed algorithm using precision/recall rates, Fβ-score and root-mean-squared distance (RMSD) and show that our algorithm provides improved detection accuracy compared to existing algorithms.

Accurate detection of individual cell nuclei in microscopic images is an essential task for many biological studies. Blur, clutter, bleed through, imaging noise and touching and partially overlapping nuclei with varying sizes and shapes make automated detection of individual cell nuclei a challenging task using image analysis. In this paper we propose an automated method for robust detection of individual cell nuclei in fluorescence in-situ hybridization (FISH) images obtained via confocal microscopy. Our algorithm consists of the following steps: image denoising, binarization, detection of nuclear seed points combining the fast radial symmetric transform (FRST) and a distance-based non-maximum suppression. We show that our algorithm provides improved detection accuracy compared to the existing algorithms.

We propose a new graph-based approach for performing a multilabel, interactive image segmentation using the principle of random walks. Using the random walk principle, given a set of user-defined (or prelabeled) pixels as labels, one can analytically calculate the probability of walking from each unlabeled pixel to each labeled pixel, thereby defining a vector of probabilities for each unlabeled pixel. By efficiently combining this vector of probabilities obtained for each unlabeled pixel, they can be assigned to one of the labels using the watershed algorithm to obtain an image segmentation. We present quantitative and qualitative results, comparing our new algorithm with the original random walker image segmentation algorithm.