Learning Enriched Features for Fast Image Restoration and Enhancement

By Syed Waqas Zamir, Aditya Arora, Salman Hameed Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022

623 citations recorded by the source corpus.

Given a degraded input image, image restoration aims to recover the missing high-quality image content. Numerous applications demand effective image restoration, e.g., computational photography, surveillance, autonomous vehicles, and remote sensing. Significant advances in image restoration have been made in recent years, dominated by convolutional neural networks (CNNs). The widely-used CNN-based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatial details are preserved but the contextual information cannot be precisely encoded. In the latter case, generated outputs are semantically reliable but spatially less accurate. This paper presents a new architecture with a holistic goal of maintaining spatially-precise high-resolution representations through the entire network, and receiving complementary contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing the following key elements: (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange across the multi-resolution streams, (c) non-local attention mechanism for capturing contextual information, and (d) attention based multi-scale feature aggregation. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Extensive experiments on six real image benchmark datasets demonstrate that our method, named as MIRNet-v2, achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement. The source code and pre-trained models are available at https://github.com/swz30/MIRNetv2.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/bb3c793a5a6d3be2454d22a76ada5b6a94ec42f5

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Citations in the corpus

OmniGen: Unified Image Generation (2024, neutral): “A man and a short-haired woman with a wrinkled face are standing in front of a bookshelf in a library; the man is the man in the middle of |image_1|, and the woman is the oldest in |image_2| A man and a woman are sitting at a classroom desk.”
Simple Baselines for Image Restoration (2022, methodology): “Method MPRNet MIRNet NBNet UFormer MAXIM HINet Restormer Baseline NAFNet [35] [38] [5] [34] [30] [4] [37] ours ours PSNR 39.”