The progress of image denoising based on FPGA

With the increasing demand for high-quality images in applications such as intelligent driving and medical imaging, the removal of noise like Gaussian and salt-and-pepper noise has become a significant challenge. Traditional software-based denoising methods, while flexible, often fail to meet real-time performance and high throughput requirements, especially with the growing scale of image data. FPGA (Field-Programmable Gate Array) provides a promising alternative with its high parallelism and low power consumption, enabling efficient hardware-level image processing. This paper aims to FPGA-based image-denoising algorithms, categorizing them into three main types: spatial domain, transformation domain, and deep learning methods. It explores their theoretical foundations, characteristics, and adaptability to FPGA platforms. In addition, optimization techniques such as data reuse, pipelining, and parallelization are discussed to improve resource efficiency and processing speed. Practical implementations highlight the advantages of FPGA in balancing computational demands and hardware constraints. While FPGA offers significant advantages, challenges remain, such as handling complex algorithms with limited resources and addressing high design complexity. The paper underscores the importance of combining hardware optimization with advanced algorithmic designs, including hybrid approaches with CPUs, to further accelerate processing. By addressing these challenges, FPGA-based solutions hold potential for advancing real-time image-denoising applications. This paper provides a comprehensive understanding of the current state of FPGA-based image denoising and serves as a reference for researchers and engineers seeking innovative solutions in this field.