Removing noise near the edge of an object in an image can be a challenging task, as any processing or filtering applied to the image can also affect the edges of the object. However, there are several approaches that can help in reducing noise while preserving the edge information:

1. Median filtering: Median filtering is a nonlinear filtering technique that can be effective in removing noise while preserving the edges. It replaces each pixel in the image with the median value of the pixels in its local neighborhood. Median filtering is particularly effective in removing salt-and-pepper noise, which appears as isolated white or black pixels in the image.

2. Gaussian filtering: Gaussian filtering is a linear filtering technique that can be used to smooth an image and reduce noise. However, it can also blur the edges of the object. To avoid blurring the edges, it is important to choose an appropriate kernel size and sigma value for the Gaussian filter.

3. Edge-preserving filters: There are several edge-preserving filters, such as the bilateral filter and the guided filter, that can reduce noise while preserving the edges of the object. These filters apply a smoothing operation to the image while taking into account the local edge information. The guided filter is particularly effective in removing noise near the edges of the object.

4. Non-local means filtering: Non-local means filtering is a powerful denoising technique that works by averaging pixels with similar texture or structure. It can effectively remove noise while preserving the edges of the object.

5. Wavelet-based denoising: Wavelet-based denoising is a signal processing technique that can be used to remove noise from an image. It decomposes the image into multiple scales and frequencies, and applies denoising to each scale separately. This approach can effectively remove noise while preserving the edge information.

It is important to note that the choice of denoising technique depends on the specific characteristics of the noise and the image. It is also important to evaluate the denoising results to ensure that the edges of the object are preserved.

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