Abstract: This Digital Image Processing Project introduces the rank residual constraint (RRC) model for rank minimization. We minimize the rank residual to progressively approximate the underlying low-rank matrix, unlike nuclear norm minimization (NNM) and weighted…
Abstract: Psychometric scaling quantifies perceptual experiences to understand the relationship between external stimulus, internal representation, and response. This Image Processing project proposes a probabilistic framework to combine rating and pairwise comparison experiments. This method can…
Abstract: Single image dehazing is ill-posed, making it difficult. Haze removal efforts are numerous for this reason. This paper proposes a simple, fast, and powerful haze removal algorithm. The medium transmission only depends on scene…
Abstract: To improve performance, multi-task learning combines related tasks. If we can identify related tasks, we can often identify unrelated tasks. Most researchers focused on exploiting correlations between interrelated tasks and ignored unrelated tasks that…
Abstract: Medical image biomarkers aid diagnosis and treatment. Supervised deep learning can accurately segment pathological regions, but it requires a priori definitions, large-scale annotations, and a representative patient cohort in the training set. Contrary to…
Abstract: HVS-based image metrics help evaluate complex image processing algorithms. However, mimicking the HVS is complex and computationally expensive (both in time and memory), limiting its use to a few applications and small input data….
Abstract: Motion, depth, and camera shake cause dynamic scene blur. Image segmentation or fully end-to-end trainable deep convolutional neural networks are used to solve this problem. Depth variations make these algorithms less effective. We propose…
Abstract: Traditional multi-view re-ranking methods compute similarity by asymmetrically matching the query image’s ROI to the target image’s ROI. This practice degrades retrieval accuracy, especially when the image ROI, which is usually interpreted as image…
Abstract: This study develops and tests a global mammographic image feature-based computer-aided diagnosis (CADx) scheme to predict malignancy. 1,959 cases were retrospectively imaged. All suspicious lesions were biopsied. 737 are malignant and 1,222 benign. Each…
Abstract: Automated breast ultrasound (ABUS) is a promising breast screening method. Unlike B-mode 2D ultrasound, ABUS provides 3D breast views and operator-independent image acquisition. ABUS image review is time-consuming and mistakes can happen. We use…
Abstract: Visualization on low dynamic range (LDR) output devices like movie screens or standard displays requires a computationally fast tone mapping operator (TMO) that can quickly adapt to a wide range of HDR content. Existing…
Abstract: Visual tracking uses object appearances and motions. Due to its superior representation power and strong learning ability, many existing techniques have recently used deep learning to improve visual tracking. Most used object appearances, but…
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