4,169 research outputs found
MDLatLRR: A novel decomposition method for infrared and visible image fusion
Image decomposition is crucial for many image processing tasks, as it allows
to extract salient features from source images. A good image decomposition
method could lead to a better performance, especially in image fusion tasks. We
propose a multi-level image decomposition method based on latent low-rank
representation(LatLRR), which is called MDLatLRR. This decomposition method is
applicable to many image processing fields. In this paper, we focus on the
image fusion task. We develop a novel image fusion framework based on MDLatLRR,
which is used to decompose source images into detail parts(salient features)
and base parts. A nuclear-norm based fusion strategy is used to fuse the detail
parts, and the base parts are fused by an averaging strategy. Compared with
other state-of-the-art fusion methods, the proposed algorithm exhibits better
fusion performance in both subjective and objective evaluation.Comment: IEEE Trans. Image Processing 2020, 14 pages, 17 figures, 3 table
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
A New Robust Multi focus image fusion Method
In today's digital era, multi focus picture fusion is a critical problem in the field of computational image processing. In the field of fusion information, multi-focus picture fusion has emerged as a significant research subject. The primary objective of multi focus image fusion is to merge graphical information from several images with various focus points into a single image with no information loss. We provide a robust image fusion method that can combine two or more degraded input photos into a single clear resulting output image with additional detailed information about the fused input images. The targeted item from each of the input photographs is combined to create a secondary image output. The action level quantities and the fusion rule are two key components of picture fusion, as is widely acknowledged. The activity level values are essentially implemented in either the "spatial domain" or the "transform domain" in most common fusion methods, such as wavelet. The brightness information computed from various source photos is compared to the laws developed to produce brightness / focus maps by using local filters to extract high-frequency characteristics. As a result, the focus map provides integrated clarity information, which is useful for a variety of Multi focus picture fusion problems. Image fusion with several modalities, for example. Completing these two jobs, on the other hand. As a consequence, we offer a strategy for achieving good fusion performance in this study paper. A Convolutional Neural Network (CNN) was trained on both high-quality and blurred picture patches to represent the mapping. The main advantage of this idea is that it can create a CNN model that can provide both the Activity level Measurement" and the Fusion rule, overcoming the limitations of previous fusion procedures. Multi focus image fusion is demonstrated using microscopic images, medical imaging, computer visualization, and Image information improvement is also a benefit of multi-focus image fusion. Greater precision is necessary in terms of target detection and identification. Face recognition" and a more compact work load, as well as enhanced system consistency, are among the new features
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