Curvelet Transform based Retinal Image Analysis

Edge detection is an important assignment in image processing, as it is used as a primary tool for pattern  recognition, image segmentation and scene analysis.  An edge detector is a high-pass filter that can be applied for extracting the edge points within an image. Edge detection in the spatial domain is  accomplished through convolution with a set of directional derivative masks in this domain. On the other hand, working in the  frequency domain has many advantages, starting from introducing an alternative description to the  spatial representation and providing more efficient and faster computational schemes with less sensitivity  to noise through high filtering, de-noising and compression algorithms. Fourier transforms, wavelet and  curvelet transform are among the most widely used frequency-domain edge detection from satellite  images. However, the Fourier transform is global and poorly adapted to local singularities. Some of  these draw backs are solved by the wavelet transforms especially for singularities detection and  computation. In this paper, the relatively new multi-resolution technique, curvelet transform, is assessed  and introduced to overcome the wavelet transform limitation in directionality and scaling.  In this research paper, the assessment of second generation curvelet transforms as an edge detection tool  will be introduced and compared with first generation cuevelet transform.DOI:http://dx.doi.org/10.11591/ijece.v3i3.245

Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm

Infrared small target detection in an infrared search and track (IRST) system is a challenging task. This situation becomes more complicated when high gray-intensity structural backgrounds appear in the field of view (FoV) of the infrared seeker. While the majority of the infrared small target detection algorithms neglect directional information, in this paper, a directional approach is presented to suppress structural backgrounds and develop a more effective detection algorithm. To this end, a similar concept to the average absolute gray difference (AAGD) is utilized to construct a novel directional small target detection algorithm called absolute directional mean difference (ADMD). Also, an efficient implementation procedure is presented for the proposed algorithm. The proposed algorithm effectively enhances the target area and eliminates background clutter. Simulation results on real infrared images prove the significant effectiveness of the proposed algorithm.Comment: The Final version (Accepted in Signal Processing journal

Directional Bilateral Filters

We propose a bilateral filter with a locally controlled domain kernel for directional edge-preserving smoothing. Traditional bilateral filters use a range kernel, which is responsible for edge preservation, and a fixed domain kernel that performs smoothing. Our intuition is that orientation and anisotropy of image structures should be incorporated into the domain kernel while smoothing. For this purpose, we employ an oriented Gaussian domain kernel locally controlled by a structure tensor. The oriented domain kernel combined with a range kernel forms the directional bilateral filter. The two kernels assist each other in effectively suppressing the influence of the outliers while smoothing. To find the optimal parameters of the directional bilateral filter, we propose the use of Stein's unbiased risk estimate (SURE). We test the capabilities of the kernels separately as well as together, first on synthetic images, and then on real endoscopic images. The directional bilateral filter has better denoising performance than the Gaussian bilateral filter at various noise levels in terms of peak signal-to-noise ratio (PSNR)