In this paper, a single image dehazing technique using dual transmission maps strategy and gradient domain guided image filtering is presented. A new strategy is adopted to compute the dual transmission maps using the dark channel and atmospheric light. Further, the transmission maps are refined to remove any remaining ill effects using the gradient-domain-guided filter. Finally, using the dark channel, atmospheric light, and refined transmission map, the haze-free image is obtained. The dual transmission maps strategy not only removes halo artifacts and reduces the saturation but also ensures the natural appearance in the recovered images. Furthermore, the proposed scheme is evaluated using a wide range of images and compared with state-of-the-art schemes. The comparison shows the superiority of the proposed technique in terms of recovering haze-free images.INDEX TERMS Image de-hazing, transmission map, gradient-domain guided image filter
Active noise control algorithms undergo stability problems in the presence of impulsive noise. This paper investigates such algorithms with online secondary path modeling for impulsive noise and varying acoustic paths. The paper presents three methods for active noise control, along with improved online secondary path modeling. Firstly, the filtered x recursive least square algorithm is applied for both active noise control and online secondary path modeling. This method gave faster convergence, improved stability, and modeling accuracy as compared to existing ones. The filtered x recursive least square algorithm is not robust for abruptly changing acoustic paths. To overcome this problem another method that uses modified gain filtered x recursive least square algorithm for active noise control is presented. Furthermore, it is observed that modified gain filtered x recursive least square achieves the desired performance with overheads of increased complexity. Thus, a hybrid method is proposed which has less computational complexity than the rest methods with no compromise on active noise control system performance.
A robust image watermarking scheme based on human visual system is proposed. The perceptual quality of the watermarked image is controlled by determining adaptive scaling factor for each individual pixel value. To determine the adaptive scaling factor, human visual system (texture masking) and fuzzy inference systems were used. The quality of the proposed scheme is tested against various attacks (Histogram equalization, rotation, Gaussian noise, scaling, cropping, JPEG compression, Y-shearing, X-shearing, median filtering, affine transformation, translation, sharpening, blurring, average filtering). The perceptual quality of watermarked image is calculated using peaksignal-to-noise-ratio, whereas the extracted watermark's quality is measured using normalized correlation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.