[CVPR 2024] Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts
Official code for 'Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers' (Computers & Graphics 2023)
Dataset created for the paper entitled 'Combining Attention Module and Pixel Shuffle for License Plate Super-resolution' (SIBGRAPI 2022)
Official code for 'Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach' (SIBGRAPI 2024)
Free and open source AI image upscaler. It uses the latest AI technology to upscale images to higher resolutions.
Use AI to enhance and upscale your low-quality images: A Google Colab notebook to inference Real-ESRGAN model
TriAttNet is an advanced image super-resolution model that utilizes a Triple Attention Mechanism to enhance image quality by learning complex dependencies across channels, spatial regions, and global structures.
MRIGAN: GAN-based MRI Image Super-Resolution
Tensor-Flow implementation of GAN trained on dataset of face images
DCGAN was used for synthetic data generation, ACGAN for classification, and SRGAN for image enhancement.
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