Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction

The correction of exposure-related issues is a pivotal component in enhancing the quality of images, offering substantial implications for various computer vision tasks. Historically, most methodologies have predominantly utilized spatial domain recovery, offering limited consideration to the potentialities of the frequency domain. Additionally, there has been a lack of a unified perspective towards low-light enhancement, exposure correction, and multi-exposure fusion, complicating and impeding the optimization of image processing. In response to these challenges, this paper proposes a novel methodology that leverages the frequency domain to improve and unify the handling of exposure correction tasks. Our method introduces Holistic Frequency Attention and Dynamic Frequency Feed-Forward Network, which replace conventional correlation computation in the spatial-domain. They form a foundational building block that facilitates a U-shaped Holistic Dynamic Frequency Transformer as a filter to extract global information and dynamically select important frequency bands for image restoration. Complementing this, we employ a Laplacian pyramid to decompose images into distinct frequency bands, followed by multiple restorers, each tuned to recover specific frequency-band information. The pyramid fusion allows a more detailed and nuanced image restoration process. Ultimately, our structure unifies the three tasks of low-light enhancement, exposure correction, and multi-exposure fusion, enabling comprehensive treatment of all classical exposure errors. Benchmarking on mainstream datasets for these tasks, our proposed method achieves state-of-the-art results, paving the way for more sophisticated and unified solutions in exposure correction

Text Detection Using Transformation Scaling Extension Algorithm in Natural Scene Images

In recent study efforts, the importance of text identification and recognition in images of natural scenes has been stressed more and more. Natural scene text contains an enormous amount of useful semantic data that can be applied in a variety of vision-related applications. The detection of shape-robust text confronts two major challenges: 1. A large number of traditional quadrangular bounding box-based detectors failed to identify text with irregular forms, making it difficult to include such text within perfect rectangles.2. Pixel-wise segmentation-based detectors sometimes struggle to identify closely positioned text examples from one another. Understanding the surroundings and extracting information from images of natural scenes depends heavily on the ability to detect and recognise text. Scene text can be aligned in a variety of ways, including vertical, curved, random, and horizontal alignments. This paper has created a novel method, the Transformation Scaling Extention Algorithm (TSEA), for text detection using a mask-scoring R-ConvNN (Region Convolutional Neural Network). This method works exceptionally well at accurately identifying text that is curved and text that has multiple orientations inside real-world input images. This study incorporates a mask-scoring R-ConvNN network framework to enhance the model's ability to score masks correctly for the observed occurrences. By providing more weight to accurate mask predictions, our scoring system eliminates inconsistencies between mask quality and score and enhances the effectiveness of instance segmentation. This paper also incorporates a Pyramid-based Text Proposal Network (PBTPN) and a Transformation Component Network (TCN) to enhance the feature extraction capabilities of the mask-scoring R-ConvNN for text identification and segmentation with the TSEA. Studies show that Pyramid Networks are especially effective in reducing false alarms caused by images with backgrounds that mimic text. On benchmark datasets ICDAR 2015, SCUT-CTW1500 containing multi-oriented and curved text, this method outperforms existing methods by conducting extensive testing across several scales and utilizing a single model. This study expands the field of vision-oriented applications by highlighting the growing significance of effectively locating and detecting text in natural situations

Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.

The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep models, and Alzheimer\u27s disease classification. Nonnegative Matrix Factorization, Autoencoder and 3D Convolutional Autoencoder are used as architectures or models for unsupervised feature learning. They are investigated along with nonnegativity, sparsity and part-based representation constraints for generalized and transferable feature extraction

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Deep learning methods for 360 monocular depth estimation and point cloud semantic segmentation

Monocular depth estimation and point cloud segmentation are essential tasks for 3D scene understanding in computer vision. Depth estimation for omnidirectional images is challenging due to the spherical distortion issue and the availability of large-scale labeled datasets. We propose two separate works for 360 monocular depth estimation tasks. In the first work, we propose a novel, model-agnostic, two-stage pipeline for omnidirectional monocular depth estimation. Our proposed framework PanoDepth takes one 360 image as input, produces one or more synthesized views in the first stage, and feeds the original image and the synthesized images into the subsequent stereo matching stage. Utilizing the explicit stereo-based geometric constraints, PanoDepth can generate dense high-quality depth. In the second work, we propose a 360 monocular depth estimation pipeline, OmniFusion, to tackle the spherical distortion issue. Our pipeline transforms a 360 image into less-distorted perspective patches (i.e. tangent images) to obtain patch-wise predictions via CNN, and then merge the patch-wise results for final output. To handle the discrepancy between patch-wise predictions which is a major issue affecting the merging quality, we propose a new framework with (i) a geometry-aware feature fusion mechanism that combines 3D geometric features with 2D image features. (ii) the self-attention-based transformer architecture to conduct a global aggregation of patch-wise information. (iii) an iterative depth refinement mechanism to further refine the estimated depth based on the more accurate geometric features. Experiments show that both PanoDepth and OmniFusion achieve state-of-the-art performances on several 360 monocular depth estimation benchmark datasets. For point cloud analysis, we mainly focus on defining effective local point convolution operators. We propose two approaches, SPNet and Point-Voxel CNN respectively. For the former, we propose a novel point convolution operator named Shell Point Convolution (SPConv) as the building block for shape encoding and local context learning. Specifically, SPConv splits 3D neighborhood space into shells, aggregates local features on manually designed kernel points, and performs convolution on the shells. For the latter, we present a novel lightweight convolutional neural network which uses point voxel convolution (PVC) layer as building block. Each PVC layer has two parallel branches, namely the voxel branch and the point branch. For the voxel branch, we aggregate local features on non-empty voxel centers to reduce geometric information loss caused by voxelization, then apply volumetric convolutions to enhance local neighborhood geometry encoding. For the point branch, we use Multi-Layer Perceptron (MLP) to extract fine-detailed point-wise features. Outputs from these two branches are adaptively fused via a feature selection module. Experimental results show that SPConv and PVC layers are effective in local shape encoding, and our proposed networks perform well in semantic segmentation tasks.Includes bibliographical references