Photographs taken by people with impaired vision frequently exhibit a combination of technical quality concerns—namely distortions—and semantic concerns—like issues with framing and aesthetic composition. To reduce the incidence of technical distortions, such as blur, poor exposure, and noise, we are developing helpful tools. Without discussing the associated aspects of semantic correctness, we postpone that topic for further work. Providing constructive feedback on the technical quality of pictures taken by visually impaired individuals is a challenging undertaking, made even harder by the prevalent, complex distortions frequently observed. With the intention of progressing the field of analyzing and measuring the technical quality of visually impaired user-generated content (VI-UGC), we created a substantial and unprecedented subjective image quality and distortion dataset. This newly developed perceptual resource, dubbed the LIVE-Meta VI-UGC Database, holds 40,000 distorted VI-UGC images from the real world, and an equal number of image patches, with which 27 million human perceptual quality judgments and distortion labels were gathered. Through the use of this psychometric resource, we developed an automatic system for predicting picture quality and distortion in images with limited vision, a system that learns the relationships between spatial quality at local and global levels. This system demonstrated superior prediction accuracy for VI-UGC images compared to existing picture quality models on this unique dataset of visually impaired images. To facilitate better picture quality and mitigate issues, we developed a prototype feedback system using a multi-task learning framework to guide users. The dataset and models are hosted at https//github.com/mandal-cv/visimpaired for convenient access.
Computer vision relies heavily on the critical and essential task of video object detection. One effective strategy to handle this task is through the aggregation of features taken from multiple frames for enhancing detection on the current frame. The standard practice of aggregating features for video object detection within readily available systems usually involves the inference of correlations between features, specifically feature-to-feature (Fea2Fea). Current methods often prove inadequate in stably estimating Fea2Fea relationships because of image degradation stemming from object occlusions, motion blur, or rare pose variations, thereby limiting the overall detection performance. This paper offers a new perspective on Fea2Fea relationships, and introduces a novel dual-level graph relation network (DGRNet) that excels at video object detection. Departing from previous methods, our DGRNet's innovative application of a residual graph convolutional network allows for simultaneous Fea2Fea relation modeling at the frame and proposal levels, leading to improved temporal feature aggregation. To enhance the graph's reliability, we introduce a node topology affinity measure that evolves the structure through the extraction of pairwise node's local topological information, thereby pruning unreliable edge connections. To the best of our knowledge, our DGRNet is the first video object detection method that utilizes dual-level graph relationships to facilitate feature aggregation. Employing the ImageNet VID dataset, our experiments reveal that DGRNet surpasses competing state-of-the-art methods. In terms of mAP, the DGRNet paired with ResNet-101 achieved 850%, and when combined with ResNeXt-101, reached 862%.
For the direct binary search (DBS) halftoning algorithm, a novel statistical ink drop displacement (IDD) printer model is developed. Page-wide inkjet printers are the intended recipients of this, especially those showing dot displacement errors. Based on the halftone pattern's structure within a local area around a pixel, the literature's tabular approach calculates the pixel's corresponding gray value. However, the difficulty in retrieving stored information and the considerable memory footprint are factors that diminish its practical implementation in printers that feature a very large number of nozzles, causing ink droplets to impact a broad area. To prevent this issue, our IDD model accounts for dot displacements by shifting each perceived ink drop in the image from its expected position to its actual position, in lieu of manipulating the average gray levels. By bypassing table lookups, DBS directly calculates the final printout's appearance. This procedure leads to the elimination of memory problems and the subsequent enhancement of computational performance. The proposed model replaces the deterministic cost function of DBS with the expected value of displacements across the ensemble, thus capturing the statistical behavior of the ink drops. The experimental results strongly suggest a noteworthy improvement in the quality of printed images, outperforming the original DBS. Comparatively, the proposed approach results in a slightly superior image quality when compared to the tabular approach.
The fundamental nature of image deblurring and its counterpoint, the blind problem, is undeniable within the context of computational imaging and computer vision. It is noteworthy that the concept of deterministic edge-preserving regularization for maximum-a-posteriori (MAP) non-blind image deblurring was quite clear a significant amount of time ago, specifically, 25 years prior. Analyses of the blind task suggest a convergence among state-of-the-art MAP methods on the characteristic of deterministic image regularization. This is frequently represented as an L0 composite style, or as an L0 plus X method, where X commonly corresponds to discriminative components like sparsity regularization stemming from dark channel features. In contrast, with a model like this, the methods of non-blind and blind deblurring are entirely unconnected. Biomphalaria alexandrina In addition, the disparate driving forces behind L0 and X pose a significant obstacle to the development of a computationally efficient numerical approach. From the outset of modern blind deblurring techniques fifteen years ago, a physically comprehensible yet practically effective and efficient regularization strategy has been a much-sought-after goal. Deterministic image regularization terms commonly employed in MAP-based blind deblurring are reconsidered in this paper, highlighting their distinctions from edge-preserving regularization techniques used in non-blind deblurring. Taking cues from the robust losses well-documented in both statistical and deep learning research, a thoughtful conjecture is then proposed. A naive approach to deterministic image regularization for blind deblurring utilizes a class of redescending potential functions (RDPs). Strikingly, the RDP-based regularization term for blind deblurring is mathematically equivalent to the first-order derivative of a non-convex regularization method for image deblurring in scenarios where the blur is known. Consequently, a close connection between the two problems arises in regularization, contrasting sharply with the conventional modeling approach to blind deblurring. Gefitinib clinical trial In the final analysis, the conjecture, supported by the principle described above, is tested on benchmark deblurring problems, and contrasted against top-performing L0+X techniques. The present context underscores the rationality and practicality of the RDP-induced regularization, with the objective of exploring a new modeling possibility for blind deblurring.
Graph convolutional architectures frequently used in human pose estimation, model the human skeleton as an undirected graph. Body joints are represented as nodes, with connections between adjacent joints forming the edges. Yet, the bulk of these approaches tend to focus on relationships between directly adjacent skeletal joints, overlooking the connections between more remote joints, thereby limiting their ability to utilize interactions between articulations far apart. We introduce a higher-order regular splitting graph network (RS-Net) for 2D-to-3D human pose estimation using matrix splitting, incorporating weight and adjacency modulation in this paper. Capturing long-range dependencies between body joints is accomplished through multi-hop neighborhoods, while also learning different modulation vectors for different joints, and including a modulation matrix added to the skeletal adjacency matrix. human microbiome Through the learnable modulation matrix, the graph structure can be adapted by including additional edges to promote the acquisition of new connections between the various body joints. In contrast to utilizing a universal weight matrix for all neighboring body joints, the RS-Net model implements weight unsharing prior to aggregating the feature vectors associated with each joint, thereby allowing for the distinct relations between them to be captured. Comparative studies, comprising experiments and ablation analyses on two benchmark datasets, validate the superior performance of our model in 3D human pose estimation, outstripping the results of recent leading methods.
Video object segmentation has recently seen remarkable advancements thanks to memory-based methods. In spite of this, segmentation performance remains limited by the propagation of errors and the utilization of excessive memory, primarily due to: 1) the semantic mismatch resulting from similarity-based matching and memory reading via heterogeneous encoding; 2) the ongoing expansion and inaccuracies of the memory pool, which directly includes all prior frame predictions. Employing Isogenous Memory Sampling and Frame-Relation mining (IMSFR), we propose a highly effective and efficient segmentation method to resolve these issues. IMSFR, utilizing an isogenous memory sampling module, continuously carries out memory matching and retrieval from sampled historical frames with the current frame in an isogenous space, reducing semantic discrepancies and accelerating model speed via a random sampling method. Additionally, to prevent the loss of vital information during the sampling process, we create a frame-relationship temporal memory module to discover connections between frames, thus maintaining the contextual data from the video sequence and reducing error accumulation.