Read and Process Camera Raw Data with MATLAB
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MatRaw: Read and Process Camera Raw Data with MATLAB

MatRaw is a simple bridge tool for extracting completely intact image data from raw files of DSLRs/DSLMs. You can use it to convert raw files (.CR2, .NEF, .ARW, .RAF, .ORF, .DNG, etc.) to OS-friendly images (.PNG, .TIFF, .PPM, etc.) or MATLAB-accessible .MAT files, and/or to perform basic processing in ISP pipeline such as raw noise reduction, white balancing, and colorimetric characterization (aka color correction or color space transformation).

Currently MatRaw only works on Windows. It uses dcraw to decode raw files. Please build or download the latest version of dcraw and place dcraw.exe to any directory listed in the PATH environment variable.

Who it's for

MatRaw would be useful for

  • 🌈 color scientists who use cameras as measuring apparatus to record objects' colors and need to access raw signal that is proportional to the radiant quantities,
  • 📷 sensor engineers who wish to evaluate sensors' performance irrespective of any post-processing,
  • 🎓 image processing researchers who work on designing demosaicking, image fusion, and denoising algorithms, and
  • 🙋 others who want to implement camera's color filter array data in a comprehensive way.

What it does

  • matrawread is the core of MatRaw, which first calls dcraw.exe in the command line with decoding only option ("-4 -D", totally raw, no darkness level subtraction, no interpolation, no white balancing, no aberration correction), and then reads the .PGM image file into the workspace. It will only apply minimum necessary processes to the raw images, for example, darkness level subtraction or fixed pattern noise (FPN) reduction, pixel response non-uniformity (PRNU) compensation (aka flat field correction), intensity normalization, and demosaicking with or without color interpolation (demosaicking without color interpolation will combine a quadruplet in color filter array into a "large" RGB pixel, rather than "guessing" the missing values). matrawread is designed for treating cameras as measuring apparatus (Photogrammetry).

  • matrawproc allows users to perform some basic processing in ISP pipeline to the raw image after matrawread, including digital signal amplification, white-balancing, and color space transformation. Images produced by matrawproc will have similar color appearance to the .JPG thumbnails saved by the camera, but in the absence of post-processing like brightness adjustment, distortion correction, color enhancement, etc. matrawproc is designed for making images appear as realistic as our eyes perceive.



  1. Build or download the latest version of dcraw and place dcraw.exe to any directory listed in the PATH environment variable, for example c:\windows\.
  2. Download sample raw images here and unzip it to \sample_raw_files\ folder. (optional but recommended)
  3. Download sample images here for FPN and PRNU calibration. (optional, only for professional users)
  4. Add .\MatRaw\ and subfolders to MATLAB path.

Basic usage

  • To read and/or convert raw files:

    converted = matrawread(raw_dir, 'param', value, ...);

    some important optional arguments:

    • cfa -- color filter array, both Bayer and X-Trans are supported.
    • interpolation -- whether to perform color interpolation.
    • darkness -- darkness level which will be subtracted from the image.
    • saturation -- saturation level for data normalization.
    • inbit/outbit -- bit depth for input/output image.
    • fpntemplate -- fixed pattern noise template.
    • prnutemplate -- pixel response non-uniformity template.
    • ......
  • To perform basic processing to the converted raw image:

    I = matrawproc(converted, 'param', value, ...);

    optional arguments:

    • scale -- amplification factor.
    • wb -- white balance method, can be 'manual', 'grayworld', or explicitly specified.
    • cam2xyz -- color matrix that converts pixel values from camera RGB color space to CIE1931 XYZ color space.
    • colorspace -- target RGB color space, both sRGB and Adobe RGB are supported.

See demo folder for more details.

⚠️ Attention

getrawparams function is able to automatically identify the darkness and saturation levels given a raw file by executing dcraw -v -d. However, darkness and saturation levels reported by dcraw may be WRONG for some camera models (e.g., Canon EOS 5D Mark IV)! Run your own calibration if the output image had a weird appearance, as done in demo2.m.


  • Q1: What are fixed pattern noise and pixel response non-uniformity?
    A1: They are sensor noise that can be calibrated in advance and removed from each target image. See this post for the comprehensive introduction. They are worth the trouble only when extremely high color accuracy is required. Feel free to ignore them if you are not sure what they are.
  • Q2: How to get a fixed pattern noise template to perform FPN reduction?
    A2: FPN template can be obtained by taking many black frames and averaging over them. See demo4 for more details.
  • Q3: How to get a pixel response non-uniformity template to perform PRNU compensation?
    A3: PRNU template can be obtained by taking multiple frames for a uniformly lit object (a black wall for instance) at the lowest ISO and about 1/2 to 1 stop down from sensor saturation, and then averaging over them after FPN subtracted. Again, see demo4 for more details.
  • Q4: Is it possible to remove image noise from other source?
    A4: Other fixed noise can be removed by appropriately passing the fpntemplate argument. For example, thermal noise can be effectively removed by replacing a fixed pattern noise template with a thermal noise template, which is exactly the procedure that is carried out by the camera when long exposure noise reduction (LENR) is enabled.


Following screenshots illustrate the rough workflow of MatRaw. Image was shot by a Nikon D3x DSLR.

Color filter array data (stored in 16-bit):

FPN removed:


PRNU compensated:

Normalized to 14-bit (output of matrawread):

Manual white balanced:

Transformed to linear sRGB color space (output of matrawproc):

Gamma corrected:


Copyright 2019 Qiu Jueqin

Licensed under MIT.