Enhancing Optical Coherence Tomography Image Quality through Residual U-Net Denoising
A Residual U-Net model effectively reduces noise and enhances the diagnostic quality of both Anterior Segment and Posterior Segment Optical Coherence Tomography images.
Efficient Multiscale Method for Image Denoising Using Nonlinear Diffusion Process with Local Denoising and Spectral Basis Functions
A multiscale method is proposed for efficient image denoising using a nonlinear diffusion process, where local denoising and spectral multiscale basis functions are employed to construct an accurate and computationally efficient coarse-scale representation.
Texture Classification Using Hilbert Curve-Based Information Quantifiers
A novel method for texture classification that transforms 2D images into 1D time series using the Hilbert curve and then computes information theory quantifiers to effectively discriminate between different texture classes.
WaveMixSR-V2: A Resource-Efficient Neural Network for Achieving State-of-the-Art Image Super-Resolution
WaveMixSR-V2 is a highly efficient neural network architecture that achieves state-of-the-art performance in image super-resolution tasks while consuming significantly fewer resources compared to other methods.
Automated Separation of Body and Background in Radiological Images: A Practical Python Implementation
This article presents a practical Python algorithm for separating the body part from the background in 2D and 3D radiological images, such as MRI and CT scans, to enable various image analysis tasks.
Efficient and Lightweight Invertible Residual Rescaling Models for High-Quality Image Reconstruction
Invertible Residual Rescaling Models (IRRM) achieve state-of-the-art performance in image rescaling tasks using a lightweight and efficient architecture.
Adapting Color Thresholds and Removing Annotations in HSV-Colored Medical Images: An Open-Source Tool for Computational Image Analysis
An open-source tool for removing letters and adapting different color thresholds in HSV-colored medical images to enable robust computational image analysis using diverse multi-center data.
Noise-Aware Illumination Interpolator for Unsupervised Low-Light Image Enhancement
A denoising-first and enhancing-later pipeline is proposed to achieve clear visibility in low-light conditions with dynamic noise, leveraging a novel noise estimation method and a learnable illumination interpolator.
Improving Texture Acutance of Digital Cameras through Hybrid Training of Denoising Networks
Hybrid training of image denoising neural networks on natural and synthetic dead leaves images can significantly improve the texture acutance metric, a standard measure of a camera's ability to preserve texture information, without impairing classic image quality metrics.
Efficient Image Fusion with State Space Model: FusionMamba
FusionMamba, an innovative method for efficient image fusion, incorporates Mamba blocks into two U-shaped networks to extract spatial and spectral features independently and hierarchically, and extends the Mamba block to accommodate dual inputs, creating a new FusionMamba block that outperforms existing fusion techniques.
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