4 research outputs found
AquaSAM: Underwater Image Foreground Segmentation
The Segment Anything Model (SAM) has revolutionized natural image
segmentation, nevertheless, its performance on underwater images is still
restricted. This work presents AquaSAM, the first attempt to extend the success
of SAM on underwater images with the purpose of creating a versatile method for
the segmentation of various underwater targets. To achieve this, we begin by
classifying and extracting various labels automatically in SUIM dataset.
Subsequently, we develop a straightforward fine-tuning method to adapt SAM to
general foreground underwater image segmentation. Through extensive experiments
involving eight segmentation tasks like human divers, we demonstrate that
AquaSAM outperforms the default SAM model especially at hard tasks like coral
reefs. AquaSAM achieves an average Dice Similarity Coefficient (DSC) of 7.13
(%) improvement and an average of 8.27 (%) on mIoU improvement in underwater
segmentation tasks
Domain Fingerprints for No-reference Image Quality Assessment
Human fingerprints are detailed and nearly unique markers of human identity.
Such a unique and stable fingerprint is also left on each acquired image. It
can reveal how an image was degraded during the image acquisition procedure and
thus is closely related to the quality of an image. In this work, we propose a
new no-reference image quality assessment (NR-IQA) approach called domain-aware
IQA (DA-IQA), which for the first time introduces the concept of domain
fingerprint to the NR-IQA field. The domain fingerprint of an image is learned
from image collections of different degradations and then used as the unique
characteristics to identify the degradation sources and assess the quality of
the image. To this end, we design a new domain-aware architecture, which
enables simultaneous determination of both the distortion sources and the
quality of an image. With the distortion in an image better characterized, the
image quality can be more accurately assessed, as verified by extensive
experiments, which show that the proposed DA-IQA performs better than almost
all the compared state-of-the-art NR-IQA methods.Comment: accepted by IEEE Transactions on Circuits and Systems for Video
Technology (TCSVT
DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning
Multi-level deep-features have been driving state-of-the-art methods for
aesthetics and image quality assessment (IQA). However, most IQA benchmarks are
comprised of artificially distorted images, for which features derived from
ImageNet under-perform. We propose a new IQA dataset and a weakly supervised
feature learning approach to train features more suitable for IQA of
artificially distorted images. The dataset, KADIS-700k, is far more extensive
than similar works, consisting of 140,000 pristine images, 25 distortions
types, totaling 700k distorted versions. Our weakly supervised feature learning
is designed as a multi-task learning type training, using eleven existing
full-reference IQA metrics as proxies for differential mean opinion scores. We
also introduce a benchmark database, KADID-10k, of artificially degraded
images, each subjectively annotated by 30 crowd workers. We make use of our
derived image feature vectors for (no-reference) image quality assessment by
training and testing a shallow regression network on this database and five
other benchmark IQA databases. Our method, termed DeepFL-IQA, performs better
than other feature-based no-reference IQA methods and also better than all
tested full-reference IQA methods on KADID-10k. For the other five benchmark
IQA databases, DeepFL-IQA matches the performance of the best existing
end-to-end deep learning-based methods on average.Comment: dataset url: http://database.mmsp-kn.d
Underwater image quality assessment: subjective and objective methods
Underwater image enhancement plays a critical role in marine industry. Various algorithms are applied to enhance underwater images, but their performance in terms of perceptual quality has been little studied. In this paper, we investigate five popular enhancement algorithms and their output image quality. To this end, we have created a benchmark, including images enhanced by different algorithms and ground truth image quality obtained by human perception experiments. We statistically analyse the impact of various enhancement algorithms on the perceived quality of underwater images. Also, the visual quality provided by these algorithms is evaluated objectively, aiming to inform the development of objective metrics for automatic assessment of the quality for underwater image enhancement. The image quality benchmark and its objective metric are made publicly available