REVIEWS OF TOPAZ JPEG TO RAW AI FULL

This is what the neural network in our brain looks like. Though they are too far away for you to make out the details with your eyes, you can imagine or remember what your friend looks like in vivid detail. It’s just like when you see a friend from far away. After training the network on enough images, the neural network will “remember” to output a higher quality image even when given a new “unseen” JPEG image as input. We train the neural network on a large number of high-quality RAW images together with their corresponding JPEG version so that it learns and remembers the correspondence. Instead of trying to reverse the information loss, which is impossible, we train a neural network to “remember” what the RAW image should look like before it’s converted to a JPEG image. approaches this problem from a very different angle. The easiest way to explain it is that A.I. How can an artificial neural network do the impossible? to enhance a JPEG image to (hopefully) RAW image quality!īut wait, I just said that it is theoretically impossible to recover the lost information. With these developments, it was natural for us to try to use A.I. Using “deep learning”, artificial neural networks have achieved some amazing results (see our AI Gigapixel and AI Clear). In the last few years, there has been some revolutionary development in Artificial Intelligence and its application in image enhancement. With the information losses in JPEG’s encoding and the fact that jpeg cameras tend to have small lenses and sensors, it is easy to conclude that it is impossible to enhance JPEG images into raw quality… well, until recently, that is. The problems only manifest when you edit the image in a strong way. When a JPEG image is saved with “Quality > 70”, it “looks” almost as good as a RAW image. To summarize the difference between JPEG and RAW:ĭon’t get me wrong. Lens/sensor → pre-processing → (8bit sRGB) → JPEG encoder → JPEG file Figure 2, camera imaging sensor size Figure 3, colorspace diagram Finally, the JPEG encoder compresses the image into an even smaller file size which results in more loss of information. This is a shame because sRGB can only represent about 50% of the colors that your eye can see, as illustrated in Figure 3. Then the images are converted to 8-bit sRGB colorspace to further reduce the amount of data in the image. This noise suppression also removes fine details in the images. In-camera pre-processing has to be used to suppress the noise to make the image looks good.

REVIEWS OF TOPAZ JPEG TO RAW AI FULL

Phone or low-cost point-and-shoot cameras have very small image sensors (the red one in figure 2 is an iPhone imager size, as opposed to DLSR full frame), which will produce much higher noise. JPEG images typically come from small cameras, like your phone, or are saved after editing to reduce image size for the internet.

Reviews of topaz jpeg to raw ai