1,182 research outputs found
Multi-exposure microscopic image fusion-based detail enhancement algorithm
[EN] Traditional microscope imaging techniques are unable to retrieve the complete dynamic range of a diatom species with complex silica-based cell walls and multi-scale patterns. In order to extract details from the diatom, multi-exposure images are captured at variable exposure settings using microscopy techniques. A recent innovation shows that image fusion overcomes the limitations of standard digital cameras to capture details from high dynamic range scene or specimen photographed using microscopy imaging techniques. In this paper, we present a cell-region sensitive exposure fusion (CS-EF) approach to produce well-exposed fused images that can be presented directly on conventional display devices. The ambition is to preserve details in poorly and brightly illuminated regions of 3-D transparent diatom shells. The aforesaid objective is achieved by taking into account local information measures, which select well-exposed regions across input exposures. In addition, a modified histogram equalization is introduced to improve uniformity of input multi-exposure image prior to fusion. Quantitative and qualitative assessment of proposed fusion results reveal better performance than several state-of-the-art algorithms that substantiate the method’s validitySIThis work was supported in part by the Spanish Government, Spain under the AQUALITAS-retos project (Ref.CTM2014-51907-C2-2-R-MINECO) and by Junta de Comunidades de Castilla-La Mancha, Spain under project HIPERDEEP (Ref. SBPLY/19/180501/000273). The funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip
A Perceptually Optimized and Self-Calibrated Tone Mapping Operator
With the increasing popularity and accessibility of high dynamic range (HDR)
photography, tone mapping operators (TMOs) for dynamic range compression are
practically demanding. In this paper, we develop a two-stage neural
network-based TMO that is self-calibrated and perceptually optimized. In Stage
one, motivated by the physiology of the early stages of the human visual
system, we first decompose an HDR image into a normalized Laplacian pyramid. We
then use two lightweight deep neural networks (DNNs), taking the normalized
representation as input and estimating the Laplacian pyramid of the
corresponding LDR image. We optimize the tone mapping network by minimizing the
normalized Laplacian pyramid distance (NLPD), a perceptual metric aligning with
human judgments of tone-mapped image quality. In Stage two, the input HDR image
is self-calibrated to compute the final LDR image. We feed the same HDR image
but rescaled with different maximum luminances to the learned tone mapping
network, and generate a pseudo-multi-exposure image stack with different detail
visibility and color saturation. We then train another lightweight DNN to fuse
the LDR image stack into a desired LDR image by maximizing a variant of the
structural similarity index for multi-exposure image fusion (MEF-SSIM), which
has been proven perceptually relevant to fused image quality. The proposed
self-calibration mechanism through MEF enables our TMO to accept uncalibrated
HDR images, while being physiology-driven. Extensive experiments show that our
method produces images with consistently better visual quality. Additionally,
since our method builds upon three lightweight DNNs, it is among the fastest
local TMOs.Comment: 20 pages,18 figure
Novel image processing algorithms and methods for improving their robustness and operational performance
Image processing algorithms have developed rapidly in recent years. Imaging functions are becoming more common in electronic devices, demanding better image quality, and more robust image capture in challenging conditions. Increasingly more complicated algorithms are being developed in order to achieve better signal to noise characteristics, more accurate colours, and wider dynamic range, in order to approach the human visual system performance levels. [Continues.
Põhjalik uuring ülisuure dünaamilise ulatusega piltide toonivastendamisest koos subjektiivsete testidega
A high dynamic range (HDR) image has a very wide range of luminance levels that
traditional low dynamic range (LDR) displays cannot visualize. For this reason, HDR
images are usually transformed to 8-bit representations, so that the alpha channel for
each pixel is used as an exponent value, sometimes referred to as exponential notation
[43]. Tone mapping operators (TMOs) are used to transform high dynamic range to
low dynamic range domain by compressing pixels so that traditional LDR display can
visualize them. The purpose of this thesis is to identify and analyse differences and
similarities between the wide range of tone mapping operators that are available in the
literature. Each TMO has been analyzed using subjective studies considering different
conditions, which include environment, luminance, and colour. Also, several inverse
tone mapping operators, HDR mappings with exposure fusion, histogram adjustment,
and retinex have been analysed in this study. 19 different TMOs have been examined
using a variety of HDR images. Mean opinion score (MOS) is calculated on those selected
TMOs by asking the opinion of 25 independent people considering candidates’
age, vision, and colour blindness
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
Non-Iterative Tone Mapping With High Efficiency and Robustness
This paper proposes an efficient approach for tone mapping, which provides a high perceptual image quality for diverse scenes. Most existing methods, optimizing images for the perceptual model, use an iterative process and this process is time consuming. To solve this problem, we proposed a new layer-based non-iterative approach to finding an optimal detail layer for generating a tone-mapped image. The proposed method consists of the following three steps. First, an image is decomposed into a base layer and a detail layer to separate the illumination and detail components. Next, the base layer is globally compressed by applying the statistical naturalness model based on the statistics of the luminance and contrast in the natural scenes. The detail layer is locally optimized based on the structure fidelity measure, representing the degree of local structural detail preservation. Finally, the proposed method constructs the final tone-mapped image by combining the resultant layers. The performance evaluation reveals that the proposed method outperforms the benchmarking methods for almost all the benchmarking test images. Specifically, the proposed method improves an average tone mapping quality index-II (TMQI-II), a feature similarity index for tone-mapped images (FSITM), and a high-dynamic range-visible difference predictor (HDR-VDP)-2.2 by up to 0.651 (223.4%), 0.088 (11.5%), and 10.371 (25.2%), respectively, compared with the benchmarking methods, whereas it improves the processing speed by over 2611 times. Furthermore, the proposed method decreases the standard deviations of TMQI-II, FSITM, and HDR-VDP-2.2, and processing time by up to 81.4%, 18.9%, 12.6%, and 99.9%, respectively, when compared with the benchmarking methods.11Ysciescopu
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