Typically, after the capturing, imaging, and transferring processes have been accomplished, the digital images will contain a variety of noise, caused by both the equipment itself and by the complex working environment. Consequently, it is necessary to perform a de-noising process to facilitate the extraction of useful information. This paper presents a fast and efficient denoising algorithm, which combines the advantages of traditional median filters and weighted filter algorithms. In this algorithm, the noise in the figure is determined, and those results are applied to adaptively change the size of the window, while assigning different weights to the pixels in the filter window. The experimental results show that we can significantly remove almost all salt and pepper noise, while retaining full image textures, edges, and other minutiae.

Drivers’ behavior analysis is increasingly becoming important in the study of intelligent transportation systems (ITS) [

In order to improve identification and to more effectively reflect objective reality, it is necessary for the digital image to go through a denoising procedure. The factors that impact the quality of the remote sensing images are the undercurrent of the Charge Coupled Device (CCD) camera, the zero response offsets, and the response inconsistency [

The impulse noise degrades the original image by replacing some image pixels by the noise value. This value could be the maximum and minimum gray level of the image that is known as salt and pepper noise, and a random pixel value in the image gray level that is random-valued impulse noise [

The first efficient method in impulse is to carry out a low-pass filter process, based on the median filtering algorithm, which is still in the core of many recent denoising methods and can removes the high-frequency portion of the image [

In this paper, we combine the advantages of these two types of algorithms (MF and WF) and propose a fast and more effective and adaptive filtering denoising algorithm. This algorithm can effectively remove the salt and pepper noise, and obtain a good denoising effect, while preserving details of the image such as textures and contours.

Shang and Sui [

A traditional median filtering algorithm is based on the type of pixels in the neighborhood and takes a gray median value to replace the original pixel [

Nair and Mol [

Calculate the median value,

Calculate the

If the template size is greater than (or equal to) the maximum value, then calculate the mean value. Otherwise, increase the template size and change its shape. If the increased result is less than

The methodology of Masood et al.’s paper [

In this paper, the advantages of the weighted filter and the improved adaptive median filter algorithms have been combined, with help from the proposed fast noise suppression algorithm, to produce an improved adaptive weighted correction algorithm. The main concept was to use weighted filters to assign different weights to the pixels in the filter window, and to utilize a median filter (for its good salt and pepper noise removal abilities), to replace the center pixel. At the same time, the concept of noise detection was introduced, and an evaluation of the previous noise point was then used to adaptively change the window size. With a premise of removing noise, improvements have been made to the signal-to-noise ratio of the postprocessing image, while preserving the textures, contours, and other feature details.

For a source image, first a point

The flowchart of the algorithm.

In this paper, we took a remote sensing image as a source, and added salt and pepper noise with different densities from 2 to 20%. Through the application of this new algorithm to the denoising process, and by taking the weighted filter algorithm, and the adaptive median filter algorithm as a comparison, the experimental results shown in Figures

(a) Remote sensing image with 10% salt and pepper noise. (b) Weight filter result (

Judging by the visual effects, the results obtained by the weighted algorithm showed that much of the noise had been removed, but the edges, contours of the runway, houses, and other small targets on the ground, had become blurred, while also losing a large amount of the texture message from the lawns and sandy lands. All of these will be detrimental to the quality and value of a collection of remote sensing information. The denoising results obtained by the adaptive median algorithm were very poor, having only removed some of the noise, and having blurred the contours of the image. Among the three groups, the experimental results obtained with this paper’s algorithm were the best. This algorithm removed almost all the noise, and retained some of the interesting features of small targets such as the tag lines on the runway and the support vehicles, and did not filter out the contours and edges of the remotely sensed images.

Judging from the evaluation index, the greatest value of peak signal-to-noise ratios was obtained by this paper’s improved algorithm, when compared with the other two algorithms discussed. With the continuously improving accuracy of the CCD imaging devices, remote sensing image noise density will remain at a low level. The experimental results in Table

Comparison of several de-noising algorithms in PSNR value.

S & P noise density | 0.02 | 0.04 | 0.06 | 0.08 | 0.1 | 0.12 | 0.14 | 0.16 | 0.18 | 0.2 |

Adaptive median filter | 27.9188 | 27.8494 | 27.7736 | 27.5341 | 27.3256 | 27.0771 | 26.9283 | 26.4809 | 26.1196 | 25.6974 |

Weight filter | 33.7074 | 29.2102 | 26.0911 | 23.7517 | 21.5619 | 20.5428 | 19.1369 | 18.0877 | 17.2054 | 16.3512 |

This paper’s algorithm | 41.3948 | 38.7249 | 37.2327 | 36.485 | 34.5214 | 33.3825 | 32.684 | 32.0604 | 31.7249 | 31.0123 |

In this paper, the advantages of the weighted filter and the improved adaptive median filter algorithms have been combined to propose an improved fast weighted-median filter algorithm. The major reason why the proposed method can significantly improve the performance of noise removing is that it adds a noise-detection before the denoising process. By this way, the procedure can quickly find out the noise pot and just directly do the removing process on the noise instead of processing the effective pixels that save a lot of time. This new algorithm can better distinguish the noise and effective information than traditional algorithms, and clean out nearly all salt and pepper noise in the remotely sensed images, while retaining some of the interesting features of small targets such as the contours, edges, and textures. The results obtained by processing with this new algorithm have a high PSNR value, as shown in Figure

PSNR graph.