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Author |
Armin Mehri |
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Title |
Deep learning based architectures for cross-domain image processing |
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2023 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Human vision is restricted to the visual-optical spectrum. Machine vision is not.
Cameras sensitive to diverse infrared spectral bands can improve the capacities of
autonomous systems and provide a comprehensive view. Relevant scene content
can be made visible, particularly in situations when sensors of other modalities,
such as a visual-optical camera, require a source of illumination. As a result, increasing the level of automation not only avoids human errors but also reduces
machine-induced errors. Furthermore, multi-spectral sensor systems with infrared
imagery as one modality are a rich source of information and can conceivably
increase the robustness of many autonomous systems. Robotics, automobiles,
biometrics, security, surveillance, and the military are some examples of fields
that can profit from the use of infrared imagery in their respective applications.
Although multimodal spectral sensors have come a long way, there are still several
bottlenecks that prevent us from combining their output information and using
them as comprehensive images. The primary issue with infrared imaging is the lack
of potential benefits due to their cost influence on sensor resolution, which grows
exponentially with greater resolution. Due to the more costly sensor technology
required for their development, their resolutions are substantially lower than thoseof regular digital cameras.
This thesis aims to improve beyond-visible-spectrum machine vision by integrating multi-modal spectral sensors. The emphasis is on transforming the produced images to enhance their resolution to match expected human perception, bring the color representation close to human understanding of natural color, and improve machine vision application performance. This research focuses mainly on two tasks, image Colorization and Image Super resolution for both single- and cross-domain problems. We first start with an extensive review of the state of the art in both tasks, point out the shortcomings of existing approaches, and then present our solutions to address their limitations. Our solutions demonstrate that low-cost channel information (i.e., visible image) can be used to improve expensive channel
information (i.e., infrared image), resulting in images with higher quality and closer to human perception at a lower cost than a high-cost infrared camera. |
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Ph.D. thesis |
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IMPRIMA |
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Angel Sappa |
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978-84-126409-1-5 |
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Admin @ si @ Meh2023 |
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3959 |
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Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Aftab Alam; Rosie Campbell; Petrus J Gerrits; Jonas Gregorio de Souza; Afifa Khan; Maria Suarez Moreno; Jack Tomaney; Rebecca C Roberts; Cameron A Petrie |
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Title |
Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan |
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Journal Article |
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2023 |
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Scientific Reports |
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ScR |
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13 |
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11257 |
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This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map. |
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Admin @ si @ BOL2023 |
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3976 |
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Author |
Xavier Soria; Angel Sappa; Patricio Humanante; Arash Akbarinia |
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Title |
Dense extreme inception network for edge detection |
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Journal Article |
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Year |
2023 |
Publication |
Pattern Recognition |
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PR |
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139 |
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109461 |
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Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we address this limitation. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs. |
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MSIAU |
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no |
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Admin @ si @ SSH2023 |
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3982 |
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Author |
Henry Velesaca; Gisel Bastidas-Guacho; Mohammad Rouhani; Angel Sappa |
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Title |
Multimodal image registration techniques: a comprehensive survey |
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2024 |
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Multimedia Tools and Applications |
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MTAP |
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This manuscript presents a review of state-of-the-art techniques proposed in the literature for multimodal image registration, addressing instances where images from different modalities need to be precisely aligned in the same reference system. This scenario arises when the images to be registered come from different modalities, among the visible and thermal spectral bands, 3D-RGB, or flash-no flash, or NIR-visible. The review spans different techniques from classical approaches to more modern ones based on deep learning, aiming to highlight the particularities required at each step in the registration pipeline when dealing with multimodal images. It is noteworthy that medical images are excluded from this review due to their specific characteristics, including the use of both active and passive sensors or the non-rigid nature of the body contained in the image. |
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MSIAU |
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Admin @ si @ VBR2024 |
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3997 |
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Patricia Suarez; Dario Carpio; Angel Sappa |
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Title |
Enhancement of guided thermal image super-resolution approaches |
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Journal Article |
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Year |
2024 |
Publication |
Neurocomputing |
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NEUCOM |
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573 |
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127197 |
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1-17 |
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Guided image processing techniques are widely used to extract meaningful information from a guiding image and facilitate the enhancement of the guided one. This paper specifically addresses the challenge of guided thermal image super-resolution, where a low-resolution thermal image is enhanced using a high-resolution visible spectrum image. We propose a new strategy that enhances outcomes from current guided super-resolution methods. This is achieved by transforming the initial guiding data into a representation resembling a thermal-like image, which is more closely in sync with the intended output. Experimental results with upscale factors of 8 and 16, demonstrate the outstanding performance of our approach in guided thermal image super-resolution obtained by mapping the original guiding information to a thermal-like image representation. |
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Admin @ si @ SCS2024 |
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3998 |
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Mohamed Ramzy Ibrahim; Robert Benavente; Daniel Ponsa; Felipe Lumbreras |
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Title |
SWViT-RRDB: Shifted Window Vision Transformer Integrating Residual in Residual Dense Block for Remote Sensing Super-Resolution |
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Conference Article |
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2024 |
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19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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Remote sensing applications, impacted by acquisition season and sensor variety, require high-resolution images. Transformer-based models improve satellite image super-resolution but are less effective than convolutional neural networks (CNNs) at extracting local details, crucial for image clarity. This paper introduces SWViT-RRDB, a new deep learning model for satellite imagery super-resolution. The SWViT-RRDB, combining transformer with convolution and attention blocks, overcomes the limitations of existing models by better representing small objects in satellite images. In this model, a pipeline of residual fusion group (RFG) blocks is used to combine the multi-headed self-attention (MSA) with residual in residual dense block (RRDB). This combines global and local image data for better super-resolution. Additionally, an overlapping cross-attention block (OCAB) is used to enhance fusion and allow interaction between neighboring pixels to maintain long-range pixel dependencies across the image. The SWViT-RRDB model and its larger variants outperform state-of-the-art (SoTA) models on two different satellite datasets in terms of PSNR and SSIM. |
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Roma; Italia; February 2024 |
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Admin @ si @ RBP2024 |
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4004 |
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Mohamed Ramzy Ibrahim; Robert Benavente; Daniel Ponsa; Felipe Lumbreras |
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Title |
Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification |
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Conference Article |
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2023 |
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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications |
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14469 |
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214–228 |
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Deep learning has made significant advances in recent years, and as a result, it is now in a stage where it can achieve outstanding results in tasks requiring visual understanding of scenes. However, its performance tends to decline when dealing with low-quality images. The advent of super-resolution (SR) techniques has started to have an impact on the field of remote sensing by enabling the restoration of fine details and enhancing image quality, which could help to increase performance in other vision tasks. However, in previous works, contradictory results for scene visual understanding were achieved when SR techniques were applied. In this paper, we present an experimental study on the impact of SR on enhancing aerial scene classification. Through the analysis of different state-of-the-art SR algorithms, including traditional methods and deep learning-based approaches, we unveil the transformative potential of SR in overcoming the limitations of low-resolution (LR) aerial imagery. By enhancing spatial resolution, more fine details are captured, opening the door for an improvement in scene understanding. We also discuss the effect of different image scales on the quality of SR and its effect on aerial scene classification. Our experimental work demonstrates the significant impact of SR on enhancing aerial scene classification compared to LR images, opening new avenues for improved remote sensing applications. |
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CIARP |
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Admin @ si @ IBP2023 |
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4008 |
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Patricia Suarez; Dario Carpio; Angel Sappa |
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Title |
Depth Map Estimation from a Single 2D Image |
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Conference Article |
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2023 |
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17th International Conference on Signal-Image Technology & Internet-Based Systems |
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347-353 |
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This paper presents an innovative architecture based on a Cycle Generative Adversarial Network (CycleGAN) for the synthesis of high-quality depth maps from monocular images. The proposed architecture leverages a diverse set of loss functions, including cycle consistency, contrastive, identity, and least square losses, to facilitate the generation of depth maps that exhibit realism and high fidelity. A notable feature of the approach is its ability to synthesize depth maps from grayscale images without the need for paired training data. Extensive comparisons with different state-of-the-art methods show the superiority of the proposed approach in both quantitative metrics and visual quality. This work addresses the challenge of depth map synthesis and offers significant advancements in the field. |
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Admin @ si @ SCS2023b |
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4009 |
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Rafael E. Rivadeneira; Henry Velesaca; Angel Sappa |
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Title |
Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach |
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2023 |
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17th International Conference on Signal-Image Technology & Internet-Based Systems |
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This work proposes a novel approach that integrates super-resolution techniques with off-the-shelf object detection methods to tackle the problem of handling very low-resolution thermal images. The suggested approach begins by enhancing the low-resolution (LR) thermal images through a guided super-resolution strategy, leveraging a high-resolution (HR) visible spectrum image. Subsequently, object detection is performed on the high-resolution thermal image. The experimental results demonstrate tremendous improvements in comparison with both scenarios: when object detection is performed on the LR thermal image alone, as well as when object detection is conducted on the up-sampled LR thermal image. Moreover, the proposed approach proves highly valuable in camouflaged scenarios where objects might remain undetected in visible spectrum images. |
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Admin @ si @ RVS2023 |
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4010 |
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Patricia Suarez; Dario Carpio; Angel Sappa |
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Title |
Boosting Guided Super-Resolution Performance with Synthesized Images |
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Conference Article |
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2023 |
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17th International Conference on Signal-Image Technology & Internet-Based Systems |
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189-195 |
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Guided image processing techniques are widely used for extracting information from a guiding image to aid in the processing of the guided one. These images may be sourced from different modalities, such as 2D and 3D, or different spectral bands, like visible and infrared. In the case of guided cross-spectral super-resolution, features from the two modal images are extracted and efficiently merged to migrate guidance information from one image, usually high-resolution (HR), toward the guided one, usually low-resolution (LR). Different approaches have been recently proposed focusing on the development of architectures for feature extraction and merging in the cross-spectral domains, but none of them care about the different nature of the given images. This paper focuses on the specific problem of guided thermal image super-resolution, where an LR thermal image is enhanced by an HR visible spectrum image. To improve existing guided super-resolution techniques, a novel scheme is proposed that maps the original guiding information to a thermal image-like representation that is similar to the output. Experimental results evaluating five different approaches demonstrate that the best results are achieved when the guiding and guided images share the same domain. |
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Admin @ si @ SCS2023c |
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4011 |
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Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution |
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2022 |
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Sensors |
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SENS |
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22 |
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6 |
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2254 |
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Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images |
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This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online. |
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3688 |
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Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Cross-spectral image dehaze through a dense stacked conditional GAN based approach |
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Conference Article |
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2018 |
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14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Infrared imaging; Dense; Stacked CGAN; Crossspectral; Convolutional networks |
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This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented
receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors
and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
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Las Palmas de Gran Canaria; November 2018 |
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978-1-5386-9385-8 |
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Notes ![sorted by Notes field, ascending order (up)](img/sort_asc.gif) |
MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ SSV2018a |
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3193 |
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Author |
Jorge Charco; Boris X. Vintimilla; Angel Sappa |
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Title |
Deep learning based camera pose estimation in multi-view environment |
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Conference Article |
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2018 |
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14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Deep learning; Camera pose estimation; Multiview environment; Siamese architecture |
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This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from
scratch on a large data set that takes as input a pair of imagesfrom the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. |
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Las Palmas de Gran Canaria; November 2018 |
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Notes ![sorted by Notes field, ascending order (up)](img/sort_asc.gif) |
MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ CVS2018 |
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3194 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Near InfraRed Imagery Colorization |
Type |
Conference Article |
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Year |
2018 |
Publication |
25th International Conference on Image Processing |
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2237 - 2241 |
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Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization |
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This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics. |
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Athens; Greece; October 2018 |
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Notes ![sorted by Notes field, ascending order (up)](img/sort_asc.gif) |
MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ SSV2018b |
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3195 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Vegetation Index Estimation from Monospectral Images |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th International Conference on Images Analysis and Recognition |
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10882 |
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353-362 |
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This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model
estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
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Povoa de Varzim; Portugal; June 2018 |
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Notes ![sorted by Notes field, ascending order (up)](img/sort_asc.gif) |
MSIAU; 600.086; 600.130; 600.122 |
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no |
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Admin @ si @ SSV2018c |
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3196 |
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