A collaborative team from five universities proposes a tree-structured convolutional neural network that leverages binary‑tree guidance, cosine cross‑domain extraction, and an adaptive Nesterov momentum optimizer to markedly improve image super‑resolution performance.
The VARSR algorithm introduces autoregressive modeling to image super‑resolution, leveraging prefix tokens, scale‑aligned rotary positional encodings, quantization error correction, and image‑quality‑guided diffusion to achieve faster inference and superior visual fidelity, as demonstrated by extensive ICML‑2025 experiments.
The paper presents a tree‑guided convolutional neural network that leverages binary‑tree structures, cosine‑based cross‑domain feature extraction, and an adaptive Nesterov momentum optimizer to enhance key layer interactions, achieving superior image super‑resolution performance as demonstrated by extensive experiments.
The second short-form video quality assessment and enhancement challenge, co‑organized by Kuaishou's audio‑video team and the Intelligent Media Computing Lab, invites global researchers to develop efficient quality assessment models and diffusion‑based super‑resolution methods using the new KwaiSR dataset, with prize money and potential CVPR workshop paper invitations.
CubeFormer introduces a novel cube attention mechanism and dual transformer blocks that dramatically improve feature diversity, enabling a lightweight image super‑resolution model to achieve state‑of‑the‑art PSNR and visual detail across multiple benchmarks while keeping parameters low.
This article presents concise English summaries of nine Kuaishou research papers accepted at ACM MM 2023, covering topics such as no‑reference video quality assessment, adaptive video quality models, blind image super‑resolution, audio‑visual‑language transfer learning, motion‑aware video diffusion, large‑scale e‑commerce retrieval, and interactive segmentation.
Microsoft Edge's newly enabled image super‑resolution feature automatically enhances picture clarity but sends image URLs to Microsoft servers, prompting privacy concerns; the article explains the feature, its local processing claim, and provides step‑by‑step instructions to disable it in both stable and Canary builds.
This article walks through a complete PyTorch implementation of the SRCNN model for image super‑resolution, covering dataset preparation, patch extraction, model architecture, training on a GTX 770 GPU for 2500 epochs, PSNR evaluation, and visual comparisons with bicubic up‑sampling.
This article reviews the seminal SRCNN paper, detailing its contributions, architecture, training pipeline, hyper‑parameters, and extensive experiments that show how a shallow fully‑convolutional network achieves superior PSNR and runtime compared to traditional sparse‑coding and bicubic methods.
Recent advances in image super-resolution leverage deep learning techniques such as convolutional neural networks, residual learning, perceptual loss, generative adversarial networks, and PixelCNN to reconstruct high-resolution details from low-resolution inputs, addressing challenges of scalability, training efficiency, and multi-scale upscaling.
