## 1 Introduction  
In the last 20 years, there has been growing interest in the use of ultrasound imaging for a variety of applications, such as observing the blood flow through an organ or other structures; determining bone density; imaging the heart, a fetus, or ocular structures; or diagnosing cancers [1; 2]. Ultrasound imaging has been widely applied owing to its ability to produce real-time images and videos. Ultrasound images are captured in real-time by transmitting high frequency sound waves through body tissue. It comprises an array of transducer elements that sequentially echo the signal for each spatial direction to generate a raw line signal. The scan is converted to construct a Cartesian image from the processed raw line signal [2]. In recent years, many researchers have attempted to develop computer-aided diagnostic (CAD) systems for diagnosing liver and breast cancers [3; 4; 5; 6] based on ultrasound imaging. The aim of these systems is to differentiate benign and malignant lesion tissues as well as cysts [7]. A CAD system carries out the diagnosis in four stages: data preprocessing, image segmentation, feature extraction, and classification [4]. Data preprocessing is the first and most vital step in the CAD system process because it reconstructs an image without eliminating the important features by reducing signal-dependent multiplicative noise called speckle [8]. The development of a precise speckle reduction model is an important step to achieve efficient denoising filter design. Recent review articles [4; 9], reported that speckle reduction filters are categorized into two broad approaches: spatial filtering and multiscale methods. Techniques under spatial domain filtering include enhanced Frost filtering [10], Lee filtering [11], mean filtering [12], Wiener filtering [13], Kuan filtering [14], and median filtering [15]. Spatial filters utilize local statistical properties to reduce speckle noise. However, small details may not be preserved [9]. Several methods [16; 17; 18; 19] use multiscale filtering, which uses the wavelet transform to preserve the image signal regardless of its frequency content. [PERSON] et al. [20] proposed reducing noise in the wavelet domain by soft thresholding. However, their approach lacked translation invariance when using the discrete wavelet transform. This is resolved by eliminating up and down samplers in the wavelet transform by using a stationary wavelet transform [21], which is a redundant technique because the number of input and output samples at each level is the same. A multiresolution technique called translation invariant image enhancement was proposed in [22]. The proposed technique incorporates noise reduction and directional filtering. Directional filtering is executed using eigenvalues by analyzing the structure of each pixel's neighborhood. [PERSON] et al. [23; 24] and [PERSON] et al. [25] proposed successful image denoising techniques called total variation (TV) and anisotropic smoothing, respectively. These models were improved and extended upon in later works [26; 27]. However, all these methods are computationally expensive. In recent years, more efficient denoising techniques such as sparse representation (SR) have been proposed [28; 29; 30; 31]. In digital image processing, many signals are sparse; i.e., they contain many coefficients either equal to or close to zero in a specific domain. The objective of SR is to efficiently reconstruct the signal with a linear combination of a few dictionary atoms from the transformed signal domain [32]. This study was conducted with the objective of developing filtering algorithm that can reduce noise without losing significant features or eliminating edges. To this end, this paper proposes, a technique that reduces the speckle noise in ultrasound imaging systems by applying a relatively new signal reconstruction model known as SR [32] to deal with complicated noise properties.