bypass edge sharpening and noise reduction). RawTherapee also makes room for basic editing tools like cropping, resizing and rotating, in addition to toggling fullscreen mode, enabling metadata filters, as well as configuring fast export options (e.g. iterations, quantity), microcontrast, impulse noise reduction, noise reduction, defringe and contrast by detail levels.Īdditionally, it is possible to adjust the white balance, vibrance, channel mixer, HSV equalizer, RGB curves, color management, demosaicing, preprocessing, raw white and black points, dark frame, flat field and chromatic aberration. set method, radius and amount), edges (e.g. So, users can modify pictures when it comes to the sharpening level (e.g. Working with multiple items at the same time is possible. Thanks to the Explorer-based layout, you can easily locate and select photographs for processing. As for the interface, RawTherapee adopts an attractive and intuitive structure. Installing the tool takes little time and minimal effort. It also features support for JPEG, BMP, TIFF and PNG, among other popular file types. Perhaps then it could shoot monochrome pictures nearly as good as monochrome cameras.RawTherapee is a Windows app that enables users to work with RAW pictures from digital cameras and make various image adjustments. Is there a paper about it? Maybe I can combine Markesteijn algorithm with this method to address the artifacts while still reduce the filesize.Īnother food for thought, maybe the center green pixels in the X-Trans array could be replace with a white pixel to improve low light response. I think this method would be a viable way to reduce post processing file size, which might be useful for Fuji's 40MP camera that's about to come out.īy the way, I would like to know more about how Markesteijn algorithm works. Despite the pixel count is reduced by half, this method doesn't seem to lose any sharpness and contrast. However, its performance is nearly identical to the fast method. The test image comes from īy my observation, this method doesn't correct the color filtering and moire artifacts like the Markesteijn algorithm. I figured out how to compare this method with Markesteijn. I'm interested to see how this method performs on actual images taken with the X-Trans sensor, or if this method has already been implemented somewhere. I don't actually have a Fuji camera, and all of the ideas above come from my Matlab simulation. Some information lost in the green channel, not very noticeable in my test.Fringing may happen on edges with high contrast.Does not restore the artifact caused by the CFA such as moire.Reduced file size without hurting sharpness.Doesn't introduce much digitus artifact that affects the texture of the subject, might be preferable for portraits.This method might be as fast as nearest neighbor if well optimized It's basically a bilinear interpolation for X-Trans sensor, while reducing file size with a little compromise. This method might be desirable for portraits. The result might seem soft, but it won't introduce digitus artifacts like worms and orange peel texture to the image. This method does not attempt to restore any artifact caused by the CFA. The difference in sharpness compared to 1:1 demosacing with Gaussian weights is negligible, despite having less than half pixel count. The fringes are also much less than fringes in a superpixeled Bayer sensor, because the RGB pixels in 3x3 grids average to the same center. It's more pleasing to the eye in my opinion. Instead, the fringes alternate in red and blue thanks to the nature of X-Trans sensor. The fringe patterns aren't like superpixel artifacts in bayer sensors, which is blue to one side and red to the other side. Except it has a little more of color fringing on high contrast edges. I've simulated it in Matlab, the result is very similar to demosaicing 1:1 pixel with Gaussian weights. We still have at least 2x information in the green channel compared to the red or the blue channel. Thus we only lose a little bit of information in the green. The input has 2x red, 5x green, 2x blue pixels, and the output has 4x red, 4x green, 4x blue pixels. Ignoring G3 will make a little more moire in my simulation, sharpness however seems a little better. We can demosaic them into the overlapping 2x2 pixel groups below:įor red and blue, we can just copy them over by the following operations:įor the green, we can divide G3 into 4 pieces, and add them to the corners: I'm not sure if it's a new idea, but I didn't find it elsewhere.īasically the 6圆 X-Trans pattern can break down into the following 3x3 pattern and its 90 degree rotation: I came up with a fast way to demosaic X-Trans sensor.
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