noise

I have fully mastered the art of Perlin Noise in 3D, and now I'm trying to use my same implementation for a 2D algorithm. The problem seems to be in picking my gradient directions. In 3D I use 16 gradients in evenly distributed directions and this works great. In 2D I figured I'd use 8 gradients. up, down, left, right, and the four diagonal directions. Here is what I get: The general look of the noise is always correct, but the edges of the squares don't quite match up. I have also tried using other gradients or fewer gradients but get similar results. Here in another example you can see that

I've found a few ways of reducing noise from image, but my task is to measure it. So I am interested in algorithm that will give me some number, noise rating. That with that number I will be able to say that one image has less noise than others. From a view of image processing, you can consult the classic paper " Image quality assessment: From error visibility to structural similarity " published in IEEE Transaction on Image Processing , which has already been cited 3000+ times according to Google Scholar. The basic idea is human's visual perception system is highly sensitive to structural

I want to develop a small Android app to measure the current noise level (probablyin dB). But I have no idea what to look for in the libs. I don't want to record the noise. Can someone give me a pointer what classes to look at? I recommend looking in these classes: android.media.AudioFormat android.media.AudioManager android.media.AudioTrack We used them in the Blinkendroid audio package a short while ago. soul For me ' maxAmplitude ' was not helpful. After day of researching I eventually reached my target. You can find my solution here: Android: AudioRecord Class Problem: Callback is never

I am using the clown.jpg image to be able to get rid of the obvious pattern/noise it has. The first step that I did before taking FFT of the image is to rescale it a square image of powers of two (i.e. 256 x 256). Using FFT and fftshift in matlab gives the fast fourier transform with the intensities centered in the image. The following image is the result of using the previous functions mentioned. I was successful to remove the pattern/noise by zeroing the "stars" manually on the FFT image as shown below: Taking the IFFT I get a much better quality of picture (not shown). The question that I

For a cellular automata project I'm working on I need to generate two dimensional boolean arrays randomly using different algorithms and techniques. At the moment I have just one type of randomization in the application - looping through every cell in the array and generating a random double variable, then if the random number is higher than 0.5 then I set that cell to true, if not it gets set to false. I would like to look into generating these boolean matrices using more interesting algorithms such as Perlin Noise or something like that. Noise generators that are used in terrain generation

Struggeling translating the position of the mouse to the location of the tiles in my grid. When it's all flat, the math looks like this: this.position.x = Math.floor(((pos.y - 240) / 24) + ((pos.x - 320) / 48)); this.position.y = Math.floor(((pos.y - 240) / 24) - ((pos.x - 320) / 48)); where pos.x and pos.y are the position of the mouse, 240 and 320 are the offset, 24 and 48 the size of the tile. Position then contains the grid coordinate of the tile I'm hovering over. This works reasonably well on a flat surface. Now I'm adding height, which the math does not take into account. This grid is a