A Digital Denoising Method Based on Data Frequency Statistical Filtering

Noise amplitude in original time domain data is usually discrete and sparse. This article presents a digital filter denoising method based on statistical frequencies of the signal values. The effective noise are identified by comparing frequency value each pixelin with preset validity discrimination threshold. Signals recognized as valid will be output directly, while signals replaced mean their surrounding pixel Compared to classical filtering methods such median filtering, this may improve recognition accuracy has potential remove random retaining details. An image reduction software statistics was developed MATLAB environment. algorithm implemented portrait density 5%~40%, efficiency compared algorithms. experimental results show that PSNR proposed new exceeds 41, reaching same level switching adaptive preceding filtering. SSIM 0.97, which better than other methods. Additionally, higher density, more obvious advantage method.