noise

问题 I'm currently using the below function to remove noise from two images of size (240x720). I'm currently computing this on my computer but would like to implement this in real time. However on my computer the function results in significant delays to the program. Is there an alternative to removing noise from an image that could work in real time? Gaussian blur perhaps? fastNlMeansDenoising(ipmOfLeftLaneRobust, ipmOfLeftLaneRobust,10,7,21); 回答1: Given a function fastNlMeansDenoising(InputArray

问题 I have implemented global illumination using the Monte Carlo method, using the scratch a pixel tutorial as a guide. My final image renders very noisy! The example below is at 64 samples, I have previously used as high as 512 and its still very noisy. Any ideas what the problem could be? Edit: Here is the output with 128 samples and 16x Super sampling,resulting in 2048 samples. Still lots of noise! 回答1: Path tracing is pretty noisy; it's the nature of the algorithm. Consider this example from

问题 I have implemented global illumination using the Monte Carlo method, using the scratch a pixel tutorial as a guide. My final image renders very noisy! The example below is at 64 samples, I have previously used as high as 512 and its still very noisy. Any ideas what the problem could be? Edit: Here is the output with 128 samples and 16x Super sampling,resulting in 2048 samples. Still lots of noise! 回答1: Path tracing is pretty noisy; it's the nature of the algorithm. Consider this example from

问题 I am attempting to add noise to a tensor that holds the greyscale pixel values of an image. I want to set a random number of the pixels values to 255. I was thinking something along the lines of: random = tf.random_normal(tf.shape(input)) mask = tf.where(tf.greater(random, 1)) and am then trying to figure out how to set the pixel values of input to 255 for every index of mask . 回答1: tf.where() can be used for this too, assigning the noise value where mask elements are True , else the original

问题 I'm trying to remove the noise in this image: aiming to isolate the triangle . Actually i have a lot of picture like that (triangle with some noise) and i don't know how can i remove noise. I want to have a great triangle without noise in order to process it (detect interest points). I have tried erode and all theses stuff but without success. Do you have an idea about how i can improve this result? 回答1: Eroding with subsequent dilation will distort the shape of your triangles. You can use

问题 I'm working on classification problem where i need to add different levels of gaussian noise to my dataset and do classification experiments until my ML algorithms can't classify the dataset. unfortunately i have no idea how to do that. any advise or coding tips on how to add the gaussian noise? 回答1: You can follow these steps: Load the data into a pandas dataframe clean_signal = pd.read_csv("data_file_name") Use numpy to generate Gaussian noise with the same dimension as the dataset. Add

问题 I need to add quantization noise to my input data. I read often these kinds of noises are modeled as noise with uniform distribution. I have an encoding/decoding network implemented with Keras (input data is time series raw data), there is a layer implemented in Keras with which you can add Gaussian noise (GaussianNoise layer), can I use this layer to create uniform noise? If not, are there other implemented layers that I can use? 回答1: You can create your own layer as such, import tensorflow

问题 I want to use the wiimote to control a cursor WITHOUT the infrared sensor bar. I'm using the pitch and roll values for this. The problem is that when the pitch is close to 0 degrees, the roll is very unstable, and when the pitch is close to 90 degrees, it is itself very unstable, but the roll is fine. Im polling the wiimote very frequently, so im averaging the last 20 values for both pitch and roll, and I can get the cursor to go where I want. The only problem is that there is still alot of

问题 I'm starting off a project experimenting with the Android microphone using code like this: mRecorder = new MediaRecorder(); mRecorder.setAudioSource(MediaRecorder.AudioSource.MIC); mRecorder.setOutputFormat(MediaRecorder.OutputFormat.THREE_GPP); mRecorder.setOutputFile(mFileName); mRecorder.setAudioEncoder(MediaRecorder.AudioEncoder.AMR_NB); After that, a prepare() and start() to begin recording mic audio. Trouble is, I'm trying to also add in some audio processing effects like

问题 I have a FPV (First Person View) receiver that shows receiving frames from a FPV camera mounted on a drone. When the transmitter is working, receiver shows a camera view. Otherwise, if connection is lost or the transmitter is not working, it shows noise frames. The noise frames have random patterns (sometimes with more white pixels and sometimes with more black pixels). I want to detect those noise frames using OpenCV in Python in an efficient way. I know that OpenCV has a method called cv2