You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Noise is the degradation of image signals caused by external sources [23]. Noise introduces random variations of brightness or color information in the captured images. Most of the time, images on the internet have some noise associated with them. As we have collected most of the data samples from different dermatology websites, the noise in our dataset is not homogeneously distributed, which made it more complex. Therefore, we applied additional filters for denoising the collected images. We started with the gaussian filter for a better image classification process. However, after using the gaussian filter, the images become completely blurred, which leads to the loss of important information and damage to the edges. We then applied the median filter, which worked better than the gaussian filter with kernel_size = 3. Though we achieved better accuracy using the bilateral filter, we got the best results while applying the non-local means filter with patch_size = 3 and patch_distance = 5. This non-local means filter preserved all the edges and reduced the noise better than the other filters for our application which is shown in Fig. 3.

Question: How does the author conclude that non-local means filter is the best filter for denoising the images ? Are there any other filters that can be used for the same task?
The author tested images on multiple filters including gaussian filter, median filter with kernel_size = 3, bilateral filter, and non-local means filter with patch_size = 3 and patch_distance = 5. Comparing with other filter non-local means filter best result by preserving all the edges and reducing noise.