The speckle noise in the imaging process of medical ultrasound imaging will be mixed with effective information, which will reduce the image quality and affect the doctor's diagnosis. Therefore, it is of great significance to study the denoising method of medical ultrasound images. Guided image filtering is a kind of edge-preserving algorithm, which can smooth the image at the same time reserving the edge of the image. However, because guided image filtering is insensitive to texture details, it can result in the loss of detailed information of the medical ultrasound image, and the fractional differential method can just compensate for this disadvantage. In order to reserve the edge features and texture features of medical images while removing noise, we propose a denoising method of medical ultrasound image based on guided image filtering and fractional derivative. Firstly, we logarithmically transform medical ultrasound images so the multiplicative noise is convert into additive noise. Then, in order to retain the detailed information of the medical ultrasound image, it is necessary to enhance its sensitivity to the texture details of the guide filter. In this paper, the image is processed with a fractional differential mask to obtain enhanced texture information, which is then imported into the guided image filter. Next, the medical ultrasound image is processed using the guided image filter containing texture information, and finally an exponential transformation is performed to obtain a denoised image. Through experiments, we can conclude that the proposed algorithm not only can effectively enhance the visual effects of ultrasound images while removing noise, but also can effectively preserve edge and texture information.
As the latest video coding standard, High Efficiency Video Coding (HEVC) achieves over 50% bit rate reduction with similar video quality compared with previous standards H.264/AVC. However, the higher compression efficiency is attained at the cost of significantly increasing computational load. In order to reduce the complexity, this paper proposes a fast coding unit (CU) partition technique to speed up the process. To detect the edge features of each CU, a more accurate improved Sobel filtering is developed and performed By analyzing the textural features of CU, an early CU splitting termination is proposed to decide whether a CU should be decomposed into four lower-dimensions CUs or not. Compared with the reference software HM16.7, experimental results indicate the proposed algorithm can lessen the encoding time up to 44.09% on average, with a negligible bit rate increase of 0.24%, and quality losses lower 0.03 dB, respectively. In addition, the proposed algorithm gets a better trade-off between complexity and rate-distortion among the other proposed works.
As the newest international video compression standard, high efficiency video coding (HEVC) achieves a higher compression ratio and better video quality, compared with the previous standard, H.264/advanced video coding. However, higher compression efficiency is obtained at the cost of extraordinary computational load, which obstructs the implementation of the HEVC encoder for real-time applications and mobile devices. Intracoding is one of the high computational stages due to the flexible coding unit (CU) sizes and high density of angular prediction modes. This paper presents an intraencoding technique to speed up the process, which is composed of an early coding tree unit (CTU) depth interval prediction and an efficient CU partition method. The encoded CU depth information in the already encoded surrounding CTUs is utilized to predict the encoding CU search depth interval of the current CTU. By analyzing the textural features of CU, an early CU splitting termination is proposed to decide whether a CU should be decomposed into four lower-dimensions CUs or not. The experimental results indicate that the proposed algorithm outperforms the reference software HM16.7 by decreasing the coding time up to 53.67% with a negligible bit rate increase of 0.52%, and peak signal-to-noise ratio losses lower 0.06 dB, respectively.