Abstract Hyperspectral image super-resolution (HSI-SR) aims to reconstruct hyperspectral images at high spatial resolution, starting from low-resolution inputs, while preserving both spatial details and spectral fidelity. In this work, we propose the Efficient Spatial-Spectral Processing Network (ESSPN), a lightweight deep learning architecture designed to address the challenges of HSI-SR in a computationally efficient way. ESSPN is built around a novel Spatial-Spectral Block (SSB) that separately models spatial structures and spectral correlations through residual convolutional and attention mechanisms. The network head incorporates an efficient upsampling module based on pixel shuffle decomposition to produce high-resolution outputs without interpolation artifacts. Extensive experiments on two publicly available datasets, i.e., ARAD1K and StereoMSI demonstrate that ESSPN achieves competitive or superior performance compared to state-of-the-art methods when evaluated at scale factors of $$\times $$ 4, $$\times $$ 6 and $$\times $$ 8. Notably, the model shows strong generalization across hyperspectral cameras with varying spectral responses, and across radiometric domains, covering both radiance and reflectance measurements, while requiring significantly fewer parameters and FLOPs compared to existing methods. These results position the proposed ESSPN as a practical and effective solution for high-quality hyperspectral image super-resolution in real-world applications.

Accurate color information plays a critical role in numerous computer vision tasks, with the Macbeth ColorChecker being a widely used reference target due to its colorimetrically characterized color patches. However, automating the precise extraction of color information in complex scenes remains a challenge. In this paper, we propose a novel method for the automatic detection and accurate extraction of color information from Macbeth ColorCheckers in challenging environments. Our approach involves two distinct phases: (i) a chart localization step using a deep learning model to identify the presence of the ColorChecker, and (ii) a consensus-based pose estimation and color extraction phase that ensures precise localization and description of individual color patches. We rigorously evaluate our method using the widely adopted NUS and ColorChecker datasets. Comparative results against state-of-the-art methods show that our method outperforms the best solution in the state of the art achieving about 5% improvement on the ColorChecker dataset and about 17% on the NUS dataset. Furthermore, the design of our approach enables it to handle the presence of multiple ColorCheckers in complex scenes. Code will be made available after pubblication at: https://github.com/LucaCogo/ColorChartLocalization . • A robust and precise method for Camera-Independent Color Chart Localization is proposed. • The method has two phases: a chart localization step, and a consensus-based pose estimation. • The method can handle the presence of multiple color targets in complex scenes. • The method outperforms the state-of-the-art solution by up to about 17% on standard datasets.

We introduce a convolutional framework (CF) for computational color constancy, building upon the established low-level image feature-based framework, which utilized simple image statistics for illuminant estimation. Our framework expands upon this through an end-to-end learnable neural architecture. This adaptation enables the learning and usage of advanced filters that are not restricted to Gaussian kernels operating on individual color channels, thus generalizing the capabilities of the original framework. Additionally, our general framework supports deeper convolutional architectures, thus increasing its computational power. It can also be efficiently applied to estimate multiple spatially varying illuminants within a single scene. Our experimental results on standard datasets demonstrate that the CF outperforms the best methods in the low-level framework, improving the illuminant estimation accuracy by up to 34% for single illuminant estimation and 30% for multiple illuminants estimation. Additionally, our framework exhibits superior performance even when the number of training images is reduced. Finally, we document the inference speedup of our implementation reaching up to $30\times $ , making the CF especially suitable for applications where efficiency is critical. Source code and trained models available at: https://github.com/MarcoBauzz/convolutional-color-constancy.

We present a novel Convolutional Neural Network that exploits the Laplacian decomposition technique, which is typically used in traditional image processing, to restore videos compressed with the High-Efficiency Video Coding (HEVC) algorithm. The proposed method decomposes the compressed frames into multi-scale frequency bands using the Laplacian decomposition, it restores each band using the ad-hoc designed Multi-frame Residual Laplacian Network (MRLN), and finally recomposes the restored bands to obtain the restored frames. By leveraging the multi-scale frequency representation of compressed frames provided by the Laplacian decomposition, MRLN can effectively reduce the compression artifacts and restore the image details with a reduced computational cost. In addition, our method can be easily instantiated in various versions to control the tradeoff between efficiency and effectiveness, representing a versatile solution for scenarios with constrained computational resources. Experimental results on the MFQEv2 benchmark dataset show that our method achieves the state-of-the-art performance in HEVC-compressed video restoration with a lower model complexity and shorter runtime with respect to existing methods. The project page is available at https://github.com/claudiom4sir/LaplacianVCAR .

Low-light video enhancement (LLVE) has received little attention compared to low-light image enhancement (LLIE) mainly due to the lack of paired low-/normal-light video datasets. Consequently, a common approach to LLVE is to enhance each video frame individually using LLIE methods. However, this practice introduces temporal inconsistencies in the resulting video. In this work, we propose a recurrent neural network (RNN) that, given a low-light video and its per-frame enhanced version, produces a temporally consistent video preserving the underlying frame-based enhancement. We achieve this by training our network with a combination of a new forward-backward temporal consistency loss and a content-preserving loss. At inference time, we can use our trained network to correct videos processed by any LLIE method. Experimental results show that our method achieves the best trade-off between temporal consistency improvement and fidelity with the per-frame enhanced video, exhibiting a lower memory complexity and comparable time complexity with respect to other state-of-the-art methods for temporal consistency.

We present a unified deep learning framework for the recognition of user identity and the recognition of imagined actions, based on electroencephalography (EEG) signals, for application as a brain–computer interface. Our solution exploits a novel shifted subsampling preprocessing step as a form of data augmentation, and a matrix representation to encode the inherent local spatial relationships of multielectrode EEG signals. The resulting image-like data are then fed to a convolutional neural network to process the local spatial dependencies, and eventually analyzed through a bidirectional long-short term memory module to focus on temporal relationships. Our solution is compared against several methods in the state of the art, showing comparable or superior performance on different tasks. Specifically, we achieve accuracy levels above 90% both for action and user classification tasks. In terms of user identification, we reach 0.39% equal error rate in the case of known users and gestures, and 6.16% in the more challenging case of unknown users and gestures. Preliminary experiments are also conducted in order to direct future works toward everyday applications relying on a reduced set of EEG electrodes.

Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.

The growing production of digital content and its dissemination across the worldwide web require eficient and precise management. In this context, image quality assessment measures (IQAMs) play a pivotal role in guiding the development of numerous image processing systems for compression, enhancement, and restoration. The structural similarity index (SSIM) is one of the most common IQAMs for estimating the similarity between a pristine reference image and its corrupted variant. The multi-scale SSIM is one of its most popular variants that allows assessing image quality at multiple spatial scales. This paper proposes a two-stage genetic programming (GP) approach to evolve novel multi-scale IQAMs, that are simultaneously more effective and efficient. We use GP to perform feature selection in the first stage, while the second stage generates the final solutions. The experimental results show that the proposed approach outperforms the existing MS-SSIM. A comprehensive analysis of the feature selection indicates that, for extracting multi-scale similarities, spatially-varying convolutions are more effective than dilated convolutions. Moreover, we provide evidence that the IQAMs learned for one database can be successfully transferred to previously unseen databases. We conclude the paper by presenting a set of evolved multi-scale IQAMs and providing their interpretation.

We present a general-purpose framework for the optimization of parametric contrast enhancement algorithms. We first define a regression module for image acceptability, which is based on deep neural features and which is trained on a large dataset of user-expressed preferences. This regression module is then used as the objective function of a Bayesian optimization process, guiding the search for the optimal parameters of a given contrast enhancement algorithm. In our experiments we optimize three different contrast enhancement algorithms of varying levels of complexity. The effectiveness of our optimization framework is experimentally confirmed by evaluating the output of the optimized contrast enhancement algorithms with respect to reference enhanced images.

In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset evaluation setup, and to present competitive results in a comparison with parametric solutions.