A new approach for atmospheric turbulence removal using low-rank matrix factorization.

In this article, a novel method for removing atmospheric turbulence from a sequence of turbulent images and restoring a high-quality image is presented. Turbulence is modeled using two factors: the geometric transformation of pixel locations represents the distortion, and the varying pixel brightness represents spatiotemporal varying blur. The main framework of the proposed method involves the utilization of low-rank matrix factorization, which achieves the modeling of both the geometric transformation of pixels and the spatiotemporal varying blur through an iterative process. In the proposed method, the initial step involves the selection of a subset of images using the random sample consensus method. Subsequently, estimation of the mixture of Gaussian noise parameters takes place. Following this, a window is chosen around each pixel based on the entropy of the surrounding region. Within this window, the transformation matrix is locally estimated. Lastly, by considering both the noise and the estimated geometric transformations of the selected images, an estimation of a low-rank matrix is conducted. This estimation process leads to the production of a turbulence-free image. The experimental results were obtained from both real and simulated datasets. These results demonstrated the efficacy of the proposed method in mitigating substantial geometrical distortions. Furthermore, the method showcased the ability to improve spatiotemporal varying blur and effectively restore the details present in the original image.

Automatic Detection of Malignant Melanoma using Macroscopic Images.

In order to distinguish between benign and malignant types of pigmented skin lesions, computerized procedures have been developed for images taken by different equipment that the most available one of them is conventional digital cameras. In this research, a new procedure to detect malignant melanoma from benign pigmented lesions using macroscopic images is presented. The images are taken by conventional digital cameras with spatial resolution higher than one megapixel and by considering no constraints and special conditions during imaging. In the proposed procedure, new methods to weaken the effect of nonuniform illumination, correction of the effect of thick hairs and large glows on the lesion and also, a new threshold-based segmentation algorithm are presented. 187 features representing asymmetry, border irregularity, color variation, diameter and texture are extracted from the lesion area and after reducing the number of features using principal component analysis (PCA), lesions are determined as malignant or benign using support vector machine classifier. According to the dermatologist diagnosis, the proposed processing methods have the ability to detect lesions area with high accuracy. The evaluation measures of classification have indicated that 13 features extracted by PCA method lead to better results than all of the extracted features. These results led to an accuracy of 82.2%, sensitivity of 77% and specificity of 86.93%. The proposed method may help dermatologists to detect the malignant lesions in the primary stages due to the minimum constraints during imaging, the ease of usage by the public and nonexperts, and high accuracy in detection of the lesion type.