PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing

PyNET-QxQ：一种用于CMOS图像传感器中QxQ拜耳图案去马赛克的高效PyNET变体

PyNET-QxQ: An Efficient PyNET Variant for QxQ Bayer Pattern Demosaicing in CMOS Image Sensors

针对移动设备深度学习ISP模型计算量大、现有去马赛克模型多适配标准拜耳CFA的问题，提出轻量QxQ拜耳去马赛克模型PyNET-QxQ及渐进蒸馏训练法，参数量不足原PyNET的2.5%且重建效果更优。

PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks

The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile camera sensor limitations. While deep learning-based approaches can efficiently solve this problem, their computational requirements usually remain too large for high-resolution on-device image processing. To address this limitation, we propose a novel PyNET-V2 Mobile CNN architecture designed specifically for edge devices, being able to process RAW 12MP photos directly on mobile phones under 1.5 second and producin…

Replacing Mobile Camera ISP with a Single Deep Learning Model

As the popularity of mobile photography is growing constantly, lots of efforts are being invested now into building complex hand-crafted camera ISP solutions. In this work, we demonstrate that even the most sophisticated ISP pipelines can be replaced with a single end-to-end deep learning model trained without any prior knowledge about the sensor and optics used in a particular device. For this, we present PyNET, a novel pyramidal CNN architecture designed for fine-grained image restoration that implicitly learns to perform all ISP steps such as image demosaicing, denoising, white balancing, c…

使用MobileNetV2增强乳腺癌肿瘤分类：图像强度、误差缓解及Streamlit驱动的实时部署的详细探索

Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment

本研究提出基于Google MobileNetV2的迁移学习模型，利用1576张超声图像进行乳腺癌肿瘤分类，实现0.82准确率和0.94 ROC-AUC，并探讨基于Streamlit的实时部署，为肿瘤诊断设定基准。