Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging

136 p.When acquired in attenuating media, digital images of ten suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasan tness for the user. In these cases, mathematical image processing reveals it self as an ideal tool to recover some of the information lost during the degradation process. In this dissertation,we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fogremoval and mammographic image processing. In the case of digital mammograms,X-ray beams traverse human tissue, and electronic detectorscapture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces low contraste dimages in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility.For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges,in this dissertation we develop new methodologies that rely on: a)physical models of the observed degradation, and b) the calculus of variations.Equipped with this powerful machinery, we design novel theoreticaland computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energie sare composed of different integral terms that are simultaneous lyminimized by means of efficient numerical schemes, producing a clean,visually-pleasant and use ful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validateour methods, confirming that the developed techniques out perform other existing approaches in the literature

CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection

Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement. For the cross-modality interaction, 1) a progressive attention guided integration unit is proposed to sufficiently integrate RGB-D feature representations in the encoder stage, and 2) a convergence aggregation structure is proposed, which flows the RGB and depth decoding features into the corresponding RGB-D decoding streams via an importance gated fusion unit in the decoder stage. For the cross-modality refinement, we insert a refinement middleware structure between the encoder and the decoder, in which the RGB, depth, and RGB-D encoder features are further refined by successively using a self-modality attention refinement unit and a cross-modality weighting refinement unit. At last, with the gradually refined features, we predict the saliency map in the decoder stage. Extensive experiments on six popular RGB-D SOD benchmarks demonstrate that our network outperforms the state-of-the-art saliency detectors both qualitatively and quantitatively.Comment: Accepted by IEEE Transactions on Image Processing 2022, 16 pages, 11 figure

Underwater image restoration: super-resolution and deblurring via sparse representation and denoising by means of marine snow removal

Underwater imaging has been widely used as a tool in many fields, however, a major issue is the quality of the resulting images/videos. Due to the light's interaction with water and its constituents, the acquired underwater images/videos often suffer from a significant amount of scatter (blur, haze) and noise. In the light of these issues, this thesis considers problems of low-resolution, blurred and noisy underwater images and proposes several approaches to improve the quality of such images/video frames. Quantitative and qualitative experiments validate the success of proposed algorithms

Visibility Recovery on Images Acquired in Attenuating Media. Application to Underwater, Fog, and Mammographic Imaging

When acquired in attenuating media, digital images often suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasantness for the user. In these cases, mathematical image processing reveals itself as an ideal tool to recover some of the information lost during the degradation process. In this dissertation, we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fog removal and mammographic image processing. In the case of digital mammograms, X-ray beams traverse human tissue, and electronic detectors capture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces lowcontrasted images in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility. For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges, in this dissertation we develop new methodologies that rely on: a) physical models of the observed degradation, and b) the calculus of variations. Equipped with this powerful machinery, we design novel theoretical and computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energies are composed of different integral terms that are simultaneously minimized by means of efficient numerical schemes, producing a clean, visually-pleasant and useful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validate our methods, confirming that the developed techniques outperform other existing approaches in the literature

CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion

Infrared and visible image fusion targets to provide an informative image by combining complementary information from different sensors. Existing learning-based fusion approaches attempt to construct various loss functions to preserve complementary features from both modalities, while neglecting to discover the inter-relationship between the two modalities, leading to redundant or even invalid information on the fusion results. To alleviate these issues, we propose a coupled contrastive learning network, dubbed CoCoNet, to realize infrared and visible image fusion in an end-to-end manner. Concretely, to simultaneously retain typical features from both modalities and remove unwanted information emerging on the fused result, we develop a coupled contrastive constraint in our loss function.In a fused imge, its foreground target/background detail part is pulled close to the infrared/visible source and pushed far away from the visible/infrared source in the representation space. We further exploit image characteristics to provide data-sensitive weights, which allows our loss function to build a more reliable relationship with source images. Furthermore, to learn rich hierarchical feature representation and comprehensively transfer features in the fusion process, a multi-level attention module is established. In addition, we also apply the proposed CoCoNet on medical image fusion of different types, e.g., magnetic resonance image and positron emission tomography image, magnetic resonance image and single photon emission computed tomography image. Extensive experiments demonstrate that our method achieves the state-of-the-art (SOTA) performance under both subjective and objective evaluation, especially in preserving prominent targets and recovering vital textural details.Comment: 25 pages, 16 figure

Neuromorphic electronics for real-time biomedical image processing

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