Edge-preserving filter with adaptive L0 gradient optimization

Wireless multimedia sensor networks have recently emerged as one of the most important technologies to actively perceive physical world and empower a wide spectrum of potential applications in various areas. Due to the advantages of rapid deployment, flexible networking, and multimedia information perceiving, wireless multimedia sensor networks are suitable for transmitting mass multimedia data such as audio, video, and images. Two-dimensional images are among the nuclear ways to convey certain information, and there exists a large number of image data to be processed and transmitted; however, the complexity of environment and the instability of sensing component both can give rise to the insignificant information of the resulted images. Hence, image processing attracts a lot of research concerns in last several decades. Our concern in this article is filtering technology on image signal. Filtering is shown to be a key technique to ensure the validity and reliability of the wireless multimedia sensor networks images, which aims to preserve salient edges and remove low-amplitude structures. The well-known L-0 gradient minimization employs L-0 norm as gradient sparsity prior, and it is capable of preserving sharp edges. Similar to the total variation model, L-0 gradient minimization may easily suffer from the staircase effect and even lose part of the structure. Therefore, in this article, we propose an edge-preserving filter with adaptive L-0 gradient optimization. Different from original L-0 gradient minimization, we introduce an adaptive L-0 regularization. The newly proposed adaptive function is feature-driven and makes the utmost of the image gradient, enabling the filter to remove low-amplitude structures and preserve key edges. Furthermore, the proposed filter can effectively avoid staircase effect and is robust to noise. We develop an efficient optimization algorithm to solve the proposed model based on alternating minimization. Through extensive experiments, our method shows many attractive properties like preserving meaningful edges, avoiding staircase effect, robustness to noise, and so on. Applications including noise reduction, clip-art compression artifact removal, detail enhancement and edge extraction, image abstraction and pencil sketching, and high dynamic range tone mapping further demonstrate the effectiveness of the proposed method.