Jingyunliang
Models by this creator
swinir
5.7K
swinir is an image restoration model based on the Swin Transformer architecture, developed by researchers at ETH Zurich. It achieves state-of-the-art performance on a variety of image restoration tasks, including classical image super-resolution, lightweight image super-resolution, real-world image super-resolution, grayscale and color image denoising, and JPEG compression artifact reduction. The model is trained on diverse datasets like DIV2K, Flickr2K, and OST, and outperforms previous state-of-the-art methods by up to 0.45 dB while reducing the parameter count by up to 67%. Model inputs and outputs swinir takes in an image and performs various image restoration tasks. The model can handle different input sizes and scales, and supports tasks like super-resolution, denoising, and JPEG artifact reduction. Inputs Image**: The input image to be restored. Task type**: The specific image restoration task to be performed, such as classical super-resolution, lightweight super-resolution, real-world super-resolution, grayscale denoising, color denoising, or JPEG artifact reduction. Scale factor**: The desired upscaling factor for super-resolution tasks. Noise level**: The noise level for denoising tasks. JPEG quality**: The JPEG quality factor for JPEG artifact reduction tasks. Outputs Restored image**: The output image with the requested restoration applied, such as a high-resolution, denoised, or JPEG artifact-reduced version of the input. Capabilities swinir is capable of performing a wide range of image restoration tasks with state-of-the-art performance. For example, it can take a low-resolution, noisy, or JPEG-compressed image and output a high-quality, clean, and artifact-free version. The model works well on a variety of image types, including natural scenes, faces, and text-heavy images. What can I use it for? swinir can be used in a variety of applications that require high-quality image restoration, such as: Enhancing the resolution and quality of low-quality images for use in social media, e-commerce, or photography. Improving the visual fidelity of images generated by GFPGAN or Codeformer for better face restoration. Reducing noise and artifacts in images captured in low-light or poor conditions for better visualization and analysis. Preprocessing images for downstream computer vision tasks like object detection or classification. Things to try One interesting thing to try with swinir is using it to restore real-world images that have been degraded by various factors, such as low resolution, noise, or JPEG artifacts. The model's ability to handle diverse degradation types and produce high-quality results makes it a powerful tool for practical image restoration applications. Another interesting experiment would be to compare swinir's performance to other state-of-the-art image restoration models like SuperPR or Swin2SR on a range of benchmark datasets and tasks. This could help understand the relative strengths and weaknesses of the different approaches.
Updated 6/25/2024
hcflow-sr
220
hcflow-sr is a powerful image super-resolution model developed by jingyunliang that can generate high-resolution images from low-resolution inputs. Unlike traditional super-resolution models that learn a deterministic mapping, hcflow-sr learns to predict diverse photo-realistic high-resolution images. This model can be applied to both general image super-resolution and face image super-resolution, achieving state-of-the-art performance in both tasks. The model is built upon the concept of normalizing flows, which can effectively model the distribution of high-frequency image components. hcflow-sr unifies image super-resolution and image rescaling in a single framework, jointly modeling the downscaling and upscaling processes. This allows the model to achieve high accuracy in both tasks. Model inputs and outputs hcflow-sr takes a low-resolution image as input and generates a high-resolution output image. The model can handle both general images and face images, with the ability to scale up the resolution by a factor of 4 or 8. Inputs image**: A low-resolution input image Outputs Output**: A high-resolution output image Capabilities hcflow-sr demonstrates impressive performance in both general image super-resolution and face image super-resolution. It can generate diverse, photo-realistic high-resolution images that are superior to those produced by traditional super-resolution models. What can I use it for? hcflow-sr can be used in a variety of applications where high-quality image upscaling is required, such as medical imaging, surveillance, and entertainment. It can also be used to enhance the resolution of low-quality face images, making it useful for applications like facial recognition and image-based authentication. Things to try With hcflow-sr, you can experiment with generating high-resolution images from low-resolution inputs, exploring the model's ability to produce diverse and realistic results. You can also compare the performance of hcflow-sr to other super-resolution models like ESRGAN and Real-ESRGAN to understand the strengths and limitations of each approach.
Updated 6/25/2024