140 research outputs found
Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function
Images captured in dusty environments suffering from poor visibility and
quality. Enhancement of these images such as sand dust images plays a critical
role in various atmospheric optics applications. In this work, proposed a new
model based on Color Correction and new membership function to enhance san dust
images. The proposed model consists of three phases: correction of color shift,
removal of haze, and enhancement of contrast and brightness. The color shift is
corrected using a new membership function to adjust the values of U and V in
the YUV color space. The Adaptive Dark Channel Prior (A-DCP) is used for haze
removal. The stretching contrast and improving image brightness are based on
Contrast Limited Adaptive Histogram Equalization (CLAHE). The proposed model
tests and evaluates through many real sand dust images. The experimental
results show that the proposed solution is outperformed the current studies in
terms of effectively removing the red and yellow cast and provides high quality
and quantity dust images
Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations
Our study aims to review and analyze the most relevant studies in the image dehazing field. Many aspects have been deemed necessary to provide a broad understanding of various studies that have been examined through surveying the existing literature. These aspects are as follows: datasets that have been used in the literature, challenges that other researchers have faced, motivations, and recommendations for diminishing the obstacles in the reported literature. A systematic protocol is employed to search all relevant articles on image dehazing, with variations in keywords, in addition to searching for evaluation and benchmark studies. The search process is established on three online databases, namely, IEEE Xplore, Web of Science (WOS), and ScienceDirect (SD), from 2008 to 2021. These indices are selected because they are sufficient in terms of coverage. Along with definition of the inclusion and exclusion criteria, we include 152 articles to the final set. A total of 55 out of 152 articles focused on various studies that conducted image dehazing, and 13 out 152 studies covered most of the review papers based on scenarios and general overviews. Finally, most of the included articles centered on the development of image dehazing algorithms based on real-time scenario (84/152) articles. Image dehazing removes unwanted visual effects and is often considered an image enhancement technique, which requires a fully automated algorithm to work under real-time outdoor applications, a reliable evaluation method, and datasets based on different weather conditions. Many relevant studies have been conducted to meet these critical requirements. We conducted objective image quality assessment experimental comparison of various image dehazing algorithms. In conclusions unlike other review papers, our study distinctly reflects different observations on image dehazing areas. We believe that the result of this study can serve as a useful guideline for practitioners who are looking for a comprehensive view on image dehazing
A Fast Sand-Dust Image Enhancement Algorithm by Blue Channel Compensation and Guided Image Filtering
P{\O}DA: Prompt-driven Zero-shot Domain Adaptation
Domain adaptation has been vastly investigated in computer vision but still
requires access to target images at train time, which might be intractable in
some uncommon conditions. In this paper, we propose the task of `Prompt-driven
Zero-shot Domain Adaptation', where we adapt a model trained on a source domain
using only a single general textual description of the target domain, i.e., a
prompt. First, we leverage a pretrained contrastive vision-language model
(CLIP) to optimize affine transformations of source features, steering them
towards target text embeddings, while preserving their content and semantics.
Second, we show that augmented features can be used to perform zero-shot domain
adaptation for semantic segmentation. Experiments demonstrate that our method
significantly outperforms CLIP-based style transfer baselines on several
datasets for the downstream task at hand. Our prompt-driven approach even
outperforms one-shot unsupervised domain adaptation on some datasets, and gives
comparable results on others. Our code is available at
https://github.com/astra-vision/PODA.Comment: Project page: https://astra-vision.github.io/PODA
Habub
Adult comic-style graphic narrative combining the genres of adventure, historical fiction, science fiction, and mystery to address themes including racial and cultural diversity
- âŠ