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76 lines
4.0 KiB
Markdown
76 lines
4.0 KiB
Markdown
# imageDenoising - Image denoising
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## Description
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This sample demonstrates two adaptive image denoising techniques: KNN and NLM, based on computation of both geometric and color distance between texels. While both techniques are implemented in the DirectX SDK using shaders, massively speeded up variation of the latter technique, taking advantage of shared memory, is implemented in addition to DirectX counterparts.
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## Key Concepts
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Image Processing
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## Supported SM Architectures
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[SM 3.5 ](https://developer.nvidia.com/cuda-gpus) [SM 3.7 ](https://developer.nvidia.com/cuda-gpus) [SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus)
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## Supported OSes
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Linux, Windows
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## Supported CPU Architecture
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x86_64, armv7l
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## CUDA APIs involved
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### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
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cudaFree, cudaGraphicsMapResources, cudaGraphicsUnregisterResource, cudaFreeArray, cudaGraphicsGLRegisterBuffer, cudaGraphicsResourceGetMappedPointer, cudaDeviceSynchronize, cudaCreateTextureObject, cudaMalloc, cudaMallocArray, cudaGLRegisterBufferObject, cudaGraphicsUnmapResources, cudaMemcpy
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## Dependencies needed to build/run
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[X11](../../../README.md#x11), [GL](../../../README.md#gl)
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## Prerequisites
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Download and install the [CUDA Toolkit 11.6](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
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Make sure the dependencies mentioned in [Dependencies]() section above are installed.
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## Build and Run
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### Windows
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The Windows samples are built using the Visual Studio IDE. Solution files (.sln) are provided for each supported version of Visual Studio, using the format:
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```
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*_vs<version>.sln - for Visual Studio <version>
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```
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Each individual sample has its own set of solution files in its directory:
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To build/examine all the samples at once, the complete solution files should be used. To build/examine a single sample, the individual sample solution files should be used.
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> **Note:** Some samples require that the Microsoft DirectX SDK (June 2010 or newer) be installed and that the VC++ directory paths are properly set up (**Tools > Options...**). Check DirectX Dependencies section for details."
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### Linux
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The Linux samples are built using makefiles. To use the makefiles, change the current directory to the sample directory you wish to build, and run make:
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```
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$ cd <sample_dir>
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$ make
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```
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The samples makefiles can take advantage of certain options:
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* **TARGET_ARCH=<arch>** - cross-compile targeting a specific architecture. Allowed architectures are x86_64, armv7l.
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By default, TARGET_ARCH is set to HOST_ARCH. On a x86_64 machine, not setting TARGET_ARCH is the equivalent of setting TARGET_ARCH=x86_64.<br/>
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`$ make TARGET_ARCH=x86_64` <br/> `$ make TARGET_ARCH=armv7l` <br/>
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See [here](http://docs.nvidia.com/cuda/cuda-samples/index.html#cross-samples) for more details.
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* **dbg=1** - build with debug symbols
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```
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$ make dbg=1
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```
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* **SMS="A B ..."** - override the SM architectures for which the sample will be built, where `"A B ..."` is a space-delimited list of SM architectures. For example, to generate SASS for SM 50 and SM 60, use `SMS="50 60"`.
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```
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$ make SMS="50 60"
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```
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* **HOST_COMPILER=<host_compiler>** - override the default g++ host compiler. See the [Linux Installation Guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements) for a list of supported host compilers.
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```
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$ make HOST_COMPILER=g++
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```
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## References (for more details)
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[whitepaper](./doc/imageDenoising.pdf)
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