Enhancing MRI Brain Images Using Contourlet Transform and Adaptive Histogram Equalization

Human brain can be viewed using MRI images. These images will be useful for physicians, only if their quality is good. We propose a new method called, Contourlet Based Two Stage Adaptive Histogram Equalization (CBTSA), that uses Nonsubsampled Contourlet Transform (NSCT) for smoothing images and adaptive histogram equalization (AHE), under two occasions, called stages, for enhancement of the low contrast MRI images. The given MRI image is fragmented into equal sized sub-images and NSCT is applied to each of the sub-images. AHE is imposed on each resultant sub-image. All processed images are merged and AHE is applied again to the merged image. The clarity of the output image obtained by our method has outperformed the output image produced by traditional methods. The quality was measured and compared using criteria like, Entropy, Absolute Mean Brightness Error (AMBE) and Peak Signal to Noise Ratio (PSNR).

Multiplicative Watermarking Method with the Visual Saliency Model Using Contourlet Transform

We have proposed an image adaptive watermarking method by using contourlet transform. Firstly, we have selected high-energy image blocks as the watermark embedding space through segmenting the original image into nonoverlapping blocks and designed a watermark embedded strength factor by taking advantage of the human visual saliency model. To achieve dynamic adjustability of the multiplicative watermark embedding parameter, the relationship between watermark embedded strength factor and watermarked image quality is developed through experiments with the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), respectively. Secondly, to detect the watermark information, the generalized Gaussian distribution (GGD) has been utilized to model the contourlet coefficients. Furthermore, positions of the blocks selected, watermark embedding factor, and watermark size have been used as side information for watermark decoding. Finally, several experiments have been conducted on eight images, and the results prove the effectiveness of the proposed watermarking approach. Concretely, our watermarking method has good imperceptibility and strong robustness when against Gaussian noise, JPEG compression, scaling, rotation, median filtering, and Gaussian filtering attack.

Optimization of the Oil Drilling Monitoring System Based on the Multisensor Image Fusion Algorithm

The working environment of the oil drilling platform is harsh, with many uncertain factors and high operating risks. During the drilling process, due to sudden formation factors or improper process operations, it is extremely easy to cause well wall instability, sticking, lost circulation, well kick, and blowout. In addition, other complicated situations and accidents have brought major challenges to drilling safety. In order to improve the technical level of oil and gas exploration and development and achieve the goal of reducing costs and increasing efficiency, it is necessary to strengthen the optimization of traditional oil drilling monitoring systems. This article summarizes the advantages and disadvantages of the existing image multiscale analysis algorithms, from wavelet transform, stationary wavelet transforms to contourlet transform, and nondownsampled contours based on the characteristics of the images collected by different sensors in the oil drilling monitoring system and the needs of practical applications. Wave transforms detailed comparison of the fusion performance of these image analysis algorithms under the same fusion rules. Aiming at the shortcoming of the large amount of calculation of nonsubsampled contourlet transform, a fast implementation algorithm (IFNSCT) is proposed. The multichannel filter bank structure is used to replace the original tree filter bank structure, which reduces the time-consuming to the original without affecting the analysis performance of the algorithm. One-half of the oil drilling monitoring efficiency has been improved.