Jorge Bacca
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View article: A Multimodal hyperspectral dataset of cocoa beans with physicochemical annotation
A Multimodal hyperspectral dataset of cocoa beans with physicochemical annotation Open
Assessing cocoa bean quality using spectral information offers a noninvasive and objective alternative to traditional, often subjective and destructive, methods. However, progress has been limited by the lack of comprehensive datasets acro…
View article: See the past: Time-Reversed Scene Reconstruction from Thermal Traces Using Visual Language Models
See the past: Time-Reversed Scene Reconstruction from Thermal Traces Using Visual Language Models Open
Recovering the past from present observations is an intriguing challenge with potential applications in forensics and scene analysis. Thermal imaging, operating in the infrared range, provides access to otherwise invisible information. Sin…
View article: Deep Learning-Based Spectral Band Selection for Spectral Imaging Tasks
Deep Learning-Based Spectral Band Selection for Spectral Imaging Tasks Open
Spectral Images (SI) are acquired at multiple wavelengths across the electromagnetic spectrum, providing information that enhances performance in tasks such as material segmentation and classification by resolving ambiguities inherent in R…
View article: Deep Learning-Based Spectral Band Selection for Spectral Imaging Tasks
Deep Learning-Based Spectral Band Selection for Spectral Imaging Tasks Open
Spectral Images (SI) are acquired at multiple wavelengths across the electromagnetic spectrum, providing information that enhances performance in tasks such as material segmentation and classification by resolving ambiguities inherent in R…
View article: Autoregressive High-Order Finite Difference Modulo Imaging: High-Dynamic Range for Computer Vision Applications
Autoregressive High-Order Finite Difference Modulo Imaging: High-Dynamic Range for Computer Vision Applications Open
High dynamic range (HDR) imaging is vital for capturing the full range of light tones in scenes, essential for computer vision tasks such as autonomous driving. Standard commercial imaging systems face limitations in capacity for well dept…
View article: Hadamard Row-Wise Generation Algorithm
Hadamard Row-Wise Generation Algorithm Open
In this paper, we introduce an efficient algorithm for generating specific Hadamard rows, addressing the memory demands of pre-computing the entire matrix. Leveraging Sylvester's recursive construction, our method generates the required $i…
View article: Designed Dithering Sign Activation for Binary Neural Networks
Designed Dithering Sign Activation for Binary Neural Networks Open
Binary Neural Networks emerged as a cost-effective and energy-efficient solution for computer vision tasks by binarizing either network weights or activations. However, common binary activations, such as the Sign activation function, abrup…
View article: Automated chronic wounds medical assessment and tracking framework based on deep learning
Automated chronic wounds medical assessment and tracking framework based on deep learning Open
Chronic wounds are a latent health problem worldwide, due to high incidence of diseases such as diabetes and Hansen. Typically, wound evolution is tracked by medical staff through visual inspection, which becomes problematic for patients i…
View article: Deep Optics Preconditioner for Modulation-free Pyramid Wavefront Sensing
Deep Optics Preconditioner for Modulation-free Pyramid Wavefront Sensing Open
The Pyramid Wavefront Sensor (PWFS) can provide with the sensitivity needed for demanding adaptive optics applications, such as imaging exoplanets using the future extremely large telescopes of over 30 meters of diameter. However, its exqu…
View article: Computational Spectral Imaging: A Contemporary Overview
Computational Spectral Imaging: A Contemporary Overview Open
Spectral imaging collects and processes information along spatial and spectral coordinates quantified in discrete voxels, which can be treated as a 3D spectral data cube. The spectral images (SIs) allow identifying objects, crops, and mate…
View article: Computational spectral imaging: a contemporary overview
Computational spectral imaging: a contemporary overview Open
Spectral imaging collects and processes information along spatial and spectral coordinates quantified in discrete voxels, which can be treated as a 3D spectral data cube. The spectral images (SIs) allow the identification of objects, crops…
View article: Deep Optical Coding Design in Computational Imaging: A data-driven framework
Deep Optical Coding Design in Computational Imaging: A data-driven framework Open
Computational optical imaging (COI) systems leverage optical coding elements (CEs) in their setups to encode a high-dimensional scene in a single or in multiple snapshots and decode it by using computational algorithms. The performance of …
View article: Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery Open
This paper proposes a non-data-driven deep neural network for spectral image recovery problems such as denoising, single hyperspectral image super-resolution, and compressive spectral imaging reconstruction. Unlike previous methods, the pr…
View article: JR2net: a joint non-linear representation and recovery network for compressive spectral imaging
JR2net: a joint non-linear representation and recovery network for compressive spectral imaging Open
Deep learning models are state-of-the-art in compressive spectral imaging (CSI) recovery. These methods use a deep neural network (DNN) as an image generator to learn non-linear mapping from compressed measurements to the spectral image. F…
View article: Deep Optical Coding Design in Computational Imaging
Deep Optical Coding Design in Computational Imaging Open
Computational optical imaging (COI) systems leverage optical coding elements (CE) in their setups to encode a high-dimensional scene in a single or multiple snapshots and decode it by using computational algorithms. The performance of COI …
View article: Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification
Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification Open
Compressive spectral imaging (CSI) has emerged as an attractive compression and sensing technique, primarily to sense spectral regions where traditional systems result in highly costly such as in the near-infrared spectrum. Recently, it ha…
View article: D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion
D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion Open
Compressive spectral imaging (CSI) has attracted significant attention since it employs synthetic apertures to codify spatial and spectral information, sensing only 2D projections of the 3D spectral image. However, these optical architectu…
View article: JR2net: A Joint Non-Linear Representation and Recovery Network for\n Compressive Spectral Imaging
JR2net: A Joint Non-Linear Representation and Recovery Network for\n Compressive Spectral Imaging Open
Deep learning models are state-of-the-art in compressive spectral imaging\n(CSI) recovery. These methods use a deep neural network (DNN) as an image\ngenerator to learn non-linear mapping from compressed measurements to the\nspectral image…
View article: Shift-variant color-coded diffractive spectral imaging system
Shift-variant color-coded diffractive spectral imaging system Open
State-of-the-art snapshot spectral imaging (SI) systems introduce color-coded apertures (CCAs) into their setups to obtain a flexible spatial-spectral modulation, allowing spectral information to be reconstructed from a set of coded measur…
View article: Classification of Cocoa Beans Based on their Level of Fermentation using Spectral Information
Classification of Cocoa Beans Based on their Level of Fermentation using Spectral Information Open
Cocoa beans are the most important raw material for the chocolate industry and an essential product for the economy of tropical countries such as Colombia. Their price mainly depends on their quality, which is determined by various aspects…
View article: Deep Coded Aperture Design: An End-to-End Approach for Computational Imaging Tasks
Deep Coded Aperture Design: An End-to-End Approach for Computational Imaging Tasks Open
Covering from photography to depth and spectral estimation, diverse computational imaging (CI) applications benefit from the versatile modulation of coded apertures (CAs). The light wave fields as space, time, or spectral can be modulated …
View article: Coupled deep learning coded aperture design for compressive image classification
Coupled deep learning coded aperture design for compressive image classification Open
A coupled deep learning approach for coded aperture design and single-pixel measurements classification is proposed. A whole neural network is trained to simultaneously optimize the binary sensing matrix of a single-pixel camera (SPC) and …
View article: Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels
Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels Open
Spectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume t…
View article: Exact Crystalline Structure Recovery in X-ray Crystallography from Coded Diffraction Patterns
Exact Crystalline Structure Recovery in X-ray Crystallography from Coded Diffraction Patterns Open
X-ray crystallography (XC) is an experimental technique used to determine three-dimensional crystalline structures. The acquired data in XC, called diffraction patterns, is the Fourier magnitudes of the unknown crystalline structure. To es…
View article: SPRSF: Sparse Phase Retrieval via Smoothing Function
SPRSF: Sparse Phase Retrieval via Smoothing Function Open
Phase retrieval (PR) is an ill-conditioned inverse problem which can be found in various science and engineering applications. Assuming sparse priority over the signal of interest, recent algorithms have been developed to solve the phase r…