license: cc-by-4.0
pipeline_tag: image-to-image
tags:
- pytorch
- super-resolution
4xRealWebPhoto_v4_dat2
Scale: 4
Architecture: DAT
Author: Philip Hofmann
License: CC-BY-4.0
Purpose: Compression Removal, Deblur, Denoise, JPEG, WEBP, Restoration
Subject: Photography
Input Type: Images
Date: 04.04.2024
Architecture Option: DAT-2
I/O Channels: 3(RGB)->3(RGB)
Dataset: Nomos8k
Dataset Size: 8492
OTF (on the fly augmentations): No
Pretrained Model: DAT_2_x4
Iterations: 243'000
Batch Size: 4-6
GT Size: 128-256
Description: 4x Upscaling Model for Photos from the Web. The dataset consists of only downscaled photos (to handle good quality), downscaled and compressed photos (uploaded to the web and compressed by service provider), and downscale, compressed, rescaled, recompressed photos (downloaded from the web and re-uploaded to the web).
Applied lens blur, realistic noise with my ludvae200 model, JPG and WEBP compression (40-95), and down_up, linear, cubic_mitchell, lanczos, gaussian and box downsampling algorithms. For details on the degradation process, check out the pdf with its explanations and visualizations.
This is basically a dat2 version of my previous 4xRealWebPhoto_v3_atd model, but trained with a bit stronger noise values, and also a single image per variant so drastically reduced training dataset size.
Showcase:
12 Slowpics Examples