System and methods for random parameter filtering

Abstract

The invention produces a higher quality image from a rendering system based on a relationship between the output of a rendering system and the parameters used to compute them. Specifically, noise is removed in rendering by estimating the functional dependency between sample features and the random inputs to the system. Mutual information is applied to a local neighborhood of samples in each part of the image. This dependency is then used to reduce the importance of certain scene features in a cross-bilateral filter, which preserves scene detail. The results produced by the invention are computed in a few minutes thereby making it reasonably robust for use in production environments.

What is claimed is: 
     
       1. A method for performing a random parameter filter Monte Carlo denoising, comprising the steps of:
 rendering one or more image samples or one or more pixels at a given sampling rate; 
 storing a vector of a plurality of scene features and one or more rendering system inputs for each image sample or each pixel; 
 saving one or more random parameters for each image sample used to calculate the image sample by a rendering system; 
 choosing the one or more image samples or the one or more pixels to process; 
 performing pre-processing processing on the one or more image samples or the one or more pixels using one or more stored features and filter parameters that are automatically adjusted for each image sample or each pixel to compute filter weights; 
 calculating a dependency of a color and a feature of the one or more random parameters and one or more rendering system inputs to obtain a calculated dependency; 
 using the calculated dependency to determine a weight for each scene feature to obtain dependency information; 
 modifying the one or more image samples using the dependency information to obtain a final modified sample; 
 filtering the final modified sample to produce one or more pixels one or more image samples or the one or more pixels using the computed filter weights to compute a filtered value; and 
 outputting a final image, wherein the final image includes the computed filtered value of each image sample or each pixel. 
 
     
     
       2. The method for performing a random parameter filter Monte Carlo denoising according to  claim 1 , wherein the choosing step further comprises the step of conducting iterations of using a block around a pixel of the image sample the one or more pixels from a large block size to a small block size. 
     
     
       3. The method for performing a random parameter filter Monte Carlo denoising according to  claim 1 , wherein the choosing step further comprises the step of selecting a random subset of image samples within each a block around the one or more pixels. 
     
     
       4. The method for performing a random parameter filter Monte Carlo denoising according to  claim 1 , wherein the performing step further comprises the step of clustering the one or more image samples or the one or more pixels into one or more groups. 
     
     
       5. The method for performing a random parameter filter Monte Carlo denoising according to  claim 4 , wherein, wherein the clustering step includes the step of calculating the a standard deviation of the a mean for the one or more pixels of the image sample. 
     
     
       6. The method for performing a random parameter filter Monte Carlo denoising according to  claim 1 , wherein the performing step further comprises the step of manipulating the vector by removing the a mean and dividing by the a standard deviation for each scene feature of the plurality of scene features, wherein each feature is a scene feature for each image sample. 
     
     
       7. The method for performing a random parameter filter Monte Carlo denoising according to claim  1  13, wherein the a dependency is a statistical dependency. 
     
     
       8. The method for performing random parameter filter Monte Carlo denoising according to  claim 1 , wherein the one or more scene features each feature is at least one selected from the group comprising consisting of: world position, surface normal, color, texture value, texture coordinate, and shader value. 
     
     
       9. The method for performing random parameter filter Monte Carlo denoising according to  claim 1 , wherein the one or more rendering system inputs is at least one selected from the group comprising consisting of: screen position and random parameter. 
     
     
       10. The method for performing a random parameter filter Monte Carlo denoising according to  claim 1 , wherein the filtering step further comprises the steps of:
 classifying the weight filter weights above a certain value to mean that the scene a feature of the plurality has little or no dependency on the one or more random parameters and the weight filter weights below a certain value to mean that the scene a feature of the plurality has a significant dependency on the one or more random parameters. 
 
     
     
       11. The method for performing Monte Carlo denoising according to claim 1 further comprising the step of reconstructing the computed filtered value of each image sample or each pixel to obtain the final image. 
     
     
       12. The method for performing Monte Carlo denoising according to claim 1, further comprising the step of saving by a rendering system one or more random parameters for each image sample or each pixel used to calculate the image sample. 
     
     
       13. The method for performing Monte Carlo denoising according to claim 12, further comprising the steps of:
 calculating a dependency of a color and a feature of the one or more random parameters and the one or more rendering system inputs to obtain a calculated dependency;   using the calculated dependency to determine a weight for each feature to obtain dependency information; and   modifying the one or more image samples or the one or more pixels using the dependency information to obtain a final modified sample.