diff --git a/README.md b/README.md index 82b275c..bf003e9 100644 --- a/README.md +++ b/README.md @@ -52,6 +52,73 @@ If feature you are interested in is not in either branch's roadmap, feel free to ### [More detailed algorithms descriptions](content/resources/README_extensions/algorithm_descriptions/README.md) +
+ +## Algorithm Recommendations: + +### Downscaling: + +Downscaling is simpler that's why it's first +In theory the best algorithm to use, supported by this APP is **PIL**'s implementation of `Lanchos` algorithm +Second best in theory is **PIL**'s implementation of `Bicubic` algorithm +In practice though the differance is that **Lanchos** will have **sharper** and **contrastier** look, + but sometimes it looks like it has some over-sharping artifacts +If you are looking for even softer look try `Area Average` implementation by **CV2** +The rest of algorithms might be used as an artistic choice, sometimes with cool and interesting results + +### Upscaling: + +If you want to quickly scale some images with anything at + least a bit better than a default bilinear scaler present in most application, + chose either: `Bicubic` or `Lanchos` +Though **Lanchos** is in theory better it sometimes looks over sharpened and over contrasted + in comparison to **Bicubic** +`FSR` can also result in better image as it better preserves the overall shape of the object in the image, + but it will add some noise and grain to the output + +If you wish to get the best possible results from the upscaling you can choose 1 of 2 path: + +- **AI for realistic images:** + There are a lot of AI algorithms to chose from but here are the best overall for realistic images: + - `DRLN` implemented by **SI**, *or `DRLN-BAM` if your scaling factor is less than 4* + **DRLN** is in theore the bst of the simple scaling AI's that do not add detail to the image. + The image will most likely look better than when scaled with classic or smart algorithms, + but at larger scales the lack of detail becomes visible :/ + - `RealESRGAN` + **RealESRGAN** adds more detail to the upscaled image and when it works, it works great! + But not so rarely it tends to over smooth the image creating flat surfaces where previously was detail + and has big tendencies to hallucinate if the input image was too small or there was not enough detail in it + **RealESRGAN** can also be used to remove the JPEG artifacts from the image :) + - `HSDBTRE` + **HSDBTRE** is a simple hybrid of the 2 algorithms above. + It starts with applying 2x **DRLN** after which comes 2x **RealESRGAN**. + +- **AI for Anime or similar contrast art-style:** + - `Anime4K` + Designed to upscale old Anime in realtime during playback. Easy to run with mostly good results. + - `RealESRGAN` + Offers a bit better contrast on the edges, but a bit worse antialiasing, while being a lot slower. + Also tends to over smooth the background, removing small detail as e.g. fences or pattern on shirts. + - `DRLN` + As it focuses on the best upscaling without adding detail, it won't destroy the visuals with many artifacts, + while still being better than **Bilinear** scaling in most playback software. + Will be the softest of all 3. + +- **Edge Detection for pixel art or Anime/similar contrast art-style:** + Most edge detection algorithms are really unique, it is really hard to choose the best ones but here we go! + - `xBRZ` + Personally one of my favourite algorithms, this is the one that inspired me to make this APP :) + Creates a palette effect when there are gradients of high frequency detail, + usually not visible on anime or similar styles. + The simplest way to describe it is that it adds 45 deg lines where there are edges, instead of blurring them. + - `Super xBR` + Works for more angles than xBRZ, but produces more blurry output + - `NEDI` + The overall bluriness and shape are similar to **Super xBR**, but it adds detail in artistic way. + May produce some visible artifacts. + The edge detection radius can be fine-tuned with `Nedi_m` config option. *(Default and recommended minimum is 4)* + +
## Installation: diff --git a/content/resources/README_extensions/quality_comparison/README.md b/content/resources/README_extensions/quality_comparison/README.md index b7e3694..a1457b8 100644 --- a/content/resources/README_extensions/quality_comparison/README.md +++ b/content/resources/README_extensions/quality_comparison/README.md @@ -116,72 +116,6 @@ images coming soon images coming soon -

- -## Recommendations: - -### Downscaling: - -Downscaling is simpler that's why it's first -In theory the best algorithm to use, supported by this APP is **PIL**'s implementation of `Lanchos` algorithm -Second best in theory is **PIL**'s implementation of `Bicubic` algorithm -In practice though the differance is that **Lanchos** will have **sharper** and **contrastier** look, - but sometimes it looks like it has some over-sharping artifacts -If you are looking for even softer look try `Area Average` implementation by **CV2** -The rest of algorithms might be used as an artistic choice, sometimes with cool and interesting results - -### Upscaling: - -If you want to quickly scale some images with anything at - least a bit better than a default bilinear scaler present in most application, - chose either: `Bicubic` or `Lanchos` -Though **Lanchos** is in theory better it sometimes looks over sharpened and over contrasted - in comparison to **Bicubic** -`FSR` can also result in better image as it better preserves the overall shape of the object in the image, - but it will add some noise and grain to the output - -If you wish to get the best possible results from the upscaling you can choose 1 of 2 path: - -- **AI for realistic images:** - There are a lot of AI algorithms to chose from but here are the best overall for realistic images: - - `DRLN` implemented by **SI**, *or `DRLN-BAM` if your scaling factor is less than 4* - **DRLN** is in theore the bst of the simple scaling AI's that do not add detail to the image. - The image will most likely look better than when scaled with classic or smart algorithms, - but at larger scales the lack of detail becomes visible :/ - - `RealESRGAN` - **RealESRGAN** adds more detail to the upscaled image and when it works, it works great! - But not so rarely it tends to over smooth the image creating flat surfaces where previously was detail - and has big tendencies to hallucinate if the input image was too small or there was not enough detail in it - **RealESRGAN** can also be used to remove the JPEG artifacts from the image :) - - `HSDBTRE` - **HSDBTRE** is a simple hybrid of the 2 algorithms above. - It starts with applying 2x **DRLN** after which comes 2x **RealESRGAN**. - -- **AI for Anime or similar contrast art-style:** - - `Anime4K` - Designed to upscale old Anime in realtime during playback. Easy to run with mostly good results. - - `RealESRGAN` - Offers a bit better contrast on the edges, but a bit worse antialiasing, while being a lot slower. - Also tends to over smooth the background, removing small detail as e.g. fences or pattern on shirts. - - `DRLN` - As it focuses on the best upscaling without adding detail, it won't destroy the visuals with many artifacts, - while still being better than **Bilinear** scaling in most playback software. - Will be the softest of all 3. - -- **Edge Detection for pixel art or Anime/similar contrast art-style:** - Most edge detection algorithms are really unique, it is really hard to choose the best ones but here we go! - - `xBRZ` - Personally one of my favourite algorithms, this is the one that inspired me to make this APP :) - Creates a palette effect when there are gradients of high frequency detail, - usually not visible on anime or similar styles. - The simplest way to describe it is that it adds 45 deg lines where there are edges, instead of blurring them. - - `Super xBR` - Works for more angles than xBRZ, but produces more blurry output - - `NEDI` - The overall bluriness and shape are similar to **Super xBR**, but it adds detail in artistic way. - May produce some visible artifacts. - The edge detection radius can be fine-tuned with `Nedi_m` config option. *(Default and recommended minimum is 4)* -
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