A detailed poster presentation at CVPR 2024 in Seattle, WA showcases research titled "Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning." The presentation, a collaboration among various institutions including The Hong Kong University of Science and Technology, Huawei Noah’s Ark Lab, Tsinghua University, and others, delves into a multilayered approach to super-resolution imaging. The top section summarizes the abstract and background, emphasizing overcoming limitations of existing super-resolution methods through a novel self-supervised learning pipeline termed "LWay." The middle section shows detailed illustrations of the methodological framework, including pipeline steps such as "LR Image Reconstruction" and "Self-supervised Learning." Diagrams illustrate the transformation and enhancement processes applied to low-resolution (LR) images. On the right side, a series of side-by-side image comparisons on real-world datasets encapsulate the qualitative improvements made by the technique, highlighting clear distinctions between high-resolution images and their degraded counterparts within blue boxes. There are also comparisons of images from old films showcasing the effectiveness of the proposed method against other existing techniques (ZSSR, DASR, LDM, etc.). The background includes the institution logos, QR code to the paper link, and the presence of affiliation & author details at the top suggesting a comprehensive and collaborative research effort. The surrounding setups and neighboring posters imply an active, engaging conference environment focused on innovative advancements in computer vision. Text transcribed from the image: CVPR JUNE 17-21, 2024 165 SEATTLE, WA HUAWEI Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning Haoyu Chen', Wenbo Li², Jinjin Gu³, Jingjing Ren', Haoze Sun, Xueyi Zou², Zhensong Zhang, Youliang Yan², Lei Zhu¹,5* 'The Hong Kong University of Science and Technology (Guangzhou) Huawei Noah's Ark Lab The University of Sydney Tsinghua University The Hong Kong University of Science and Technology urce Code: SSM PSNR SSMPSESSIM 04 02180286571-437 +0.004 (423) 87 4311634-36003 +1.25 5404L6-122 6.977-689-0.306 8-43009344450306975-827-0.002 Background For image super-resolution (SR), bridging the gap between the performance on synthetic datasets and real-world degradation scenarios remains a challenge. Abstract This work introduces a novel "Low-Res Leads the Way" (LWay) training framework, merging Supervised Pre-training with Self-supervised Learning to enhance the adaptability of SR models to real-world images. mted Samples 322 baseline + 12.02 11.M 316- 0.25 0.50 0.75 100 125 150 Training Iterations led SL space on synthetic data Unseen High quality Real-world Image Low fidelity SSL space on real test data SL space on synthetic data PSSL space on real test data Paper Link Low quality High fidelity Ground Truth High quality High fidelity LWay combine the benefits of supervised learning (SL) on synthetic data and self-supervised learning (SSL) on the unseen test Trainable Frozen HR LR Step 1: LR Reconstruction Pre-training Target LR Degradation Encoder Reconstructor R E Degradation Embedding e Off-the-shelf SR Network S Franzen Parameters Trainable Parameters Degradation Encoder Degradation Embedding e Ci Reconstructed LR CLPIPS DWT Reconstructed Target LR Reconstructed Target LR High-frequency weight CLPIPS Real-ESAGAN Real-ESRGAN+ Way 69RGAN LWay SavR-GAN-LWay CVPR JUNE 17-21, 2024 SEATTLE, WA BSRGAN BSRGAN LWay FeMaSR FeMSR LWay StableSR StableSR-LWay SwiniR-GAN SwiniR-GAN LWP Step 2: Zero-shot Self-supervised Learning SR The proposed training pipeline LWay consists of two steps. Target LR Step 1, we pre-train a LR reconstruction network to capture degradation embedding from LR images. This embedding is then applied to HR images, regenerating LR content. Step 2, for test images, a pre-trained SR model generates SR outputs, which are then degraded by the fixed LR reconstruction network. We iteratively update the SR model using a self-supervised learning loss applied to LR images, with a focus on high-frequency details through weighted loss. This refinement process enhances the SR model's generalization performance on unseen images. Qualitative comparisons on real-world datasets. The content within the blue box represents a zoomed-in image. images, achieve high quality and LR BSRGAN SL Space iteration →SSL Space HR high fidelity SR results The SR model advances through the proposed fine-tuning iterations, moving from the supervised learning (SL) space of synthetic degradation to the self-supervised learning (SSL) space learned from test images. LR ZSSR DASR LDM DiffBIR StableSR DARSR CAL GAN LWay (Ours) Qualitative comparisons on two old films.