350 research outputs found
CNN Injected Transformer for Image Exposure Correction
Capturing images with incorrect exposure settings fails to deliver a
satisfactory visual experience. Only when the exposure is properly set, can the
color and details of the images be appropriately preserved. Previous exposure
correction methods based on convolutions often produce exposure deviation in
images as a consequence of the restricted receptive field of convolutional
kernels. This issue arises because convolutions are not capable of capturing
long-range dependencies in images accurately. To overcome this challenge, we
can apply the Transformer to address the exposure correction problem,
leveraging its capability in modeling long-range dependencies to capture global
representation. However, solely relying on the window-based Transformer leads
to visually disturbing blocking artifacts due to the application of
self-attention in small patches. In this paper, we propose a CNN Injected
Transformer (CIT) to harness the individual strengths of CNN and Transformer
simultaneously. Specifically, we construct the CIT by utilizing a window-based
Transformer to exploit the long-range interactions among different regions in
the entire image. Within each CIT block, we incorporate a channel attention
block (CAB) and a half-instance normalization block (HINB) to assist the
window-based self-attention to acquire the global statistics and refine local
features. In addition to the hybrid architecture design for exposure
correction, we apply a set of carefully formulated loss functions to improve
the spatial coherence and rectify potential color deviations. Extensive
experiments demonstrate that our image exposure correction method outperforms
state-of-the-art approaches in terms of both quantitative and qualitative
metrics
Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction
Photographs taken with less-than-ideal exposure settings often display poor
visual quality. Since the correction procedures vary significantly, it is
difficult for a single neural network to handle all exposure problems.
Moreover, the inherent limitations of convolutions, hinder the models ability
to restore faithful color or details on extremely over-/under- exposed regions.
To overcome these limitations, we propose a Macro-Micro-Hierarchical
transformer, which consists of a macro attention to capture long-range
dependencies, a micro attention to extract local features, and a hierarchical
structure for coarse-to-fine correction. In specific, the complementary
macro-micro attention designs enhance locality while allowing global
interactions. The hierarchical structure enables the network to correct
exposure errors of different scales layer by layer. Furthermore, we propose a
contrast constraint and couple it seamlessly in the loss function, where the
corrected image is pulled towards the positive sample and pushed away from the
dynamically generated negative samples. Thus the remaining color distortion and
loss of detail can be removed. We also extend our method as an image enhancer
for low-light face recognition and low-light semantic segmentation. Experiments
demonstrate that our approach obtains more attractive results than
state-of-the-art methods quantitatively and qualitatively.Comment: Accepted by ACM MM 202
Progressive Joint Low-light Enhancement and Noise Removal for Raw Images
Low-light imaging on mobile devices is typically challenging due to
insufficient incident light coming through the relatively small aperture,
resulting in a low signal-to-noise ratio. Most of the previous works on
low-light image processing focus either only on a single task such as
illumination adjustment, color enhancement, or noise removal; or on a joint
illumination adjustment and denoising task that heavily relies on short-long
exposure image pairs collected from specific camera models, and thus these
approaches are less practical and generalizable in real-world settings where
camera-specific joint enhancement and restoration is required. To tackle this
problem, in this paper, we propose a low-light image processing framework that
performs joint illumination adjustment, color enhancement, and denoising.
Considering the difficulty in model-specific data collection and the ultra-high
definition of the captured images, we design two branches: a coefficient
estimation branch as well as a joint enhancement and denoising branch. The
coefficient estimation branch works in a low-resolution space and predicts the
coefficients for enhancement via bilateral learning, whereas the joint
enhancement and denoising branch works in a full-resolution space and
progressively performs joint enhancement and denoising. In contrast to existing
methods, our framework does not need to recollect massive data when being
adapted to another camera model, which significantly reduces the efforts
required to fine-tune our approach for practical usage. Through extensive
experiments, we demonstrate its great potential in real-world low-light imaging
applications when compared with current state-of-the-art methods
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
3D scanning, modelling and printing of ultra-thin nacreous shells from Jericho: a case study of small finds documentation in archaeology
This paper springs out from a collaborative project jointly carried out by the FabLab Saperi&Co and the Museum of Near East, Egypt and Mediterranean of Sapienza University of Rome focused at producing replicas of ultra-thin archeological finds with a sub-millimetric precision. The main technological challenge of this project was to produce models through 3D optical scanning (photogrammetry) and to print faithful replicas with additive manufacturing.
The objects chosen for the trial were five extremely fragile and ultra-thin nacreous shells retrieved in Tell es-Sultan/ancient Jericho by the Italian-Palestinian Expedition in spring 2017, temporarily on exhibit in the Museum. The experiment proved to be successful, and the scanning, modeling and printing of the shells also allowed some observations on their possible uses in research and museum activities
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