1,490 research outputs found
A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function
This paper proposes a novel image contrast enhancement method based on both a
noise aware shadow-up function and Retinex (retina and cortex) decomposition.
Under low light conditions, images taken by digital cameras have low contrast
in dark or bright regions. This is due to a limited dynamic range that imaging
sensors have. For this reason, various contrast enhancement methods have been
proposed. Our proposed method can enhance the contrast of images without not
only over-enhancement but also noise amplification. In the proposed method, an
image is decomposed into illumination layer and reflectance layer based on the
retinex theory, and lightness information of the illumination layer is
adjusted. A shadow-up function is used for preventing over-enhancement. The
proposed mapping function, designed by using a noise aware histogram, allows
not only to enhance contrast of dark region, but also to avoid amplifying
noise, even under strong noise environments.Comment: To appear in IWAIT-IFMIA 201
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Self-Reference Deep Adaptive Curve Estimation for Low-Light Image Enhancement
In this paper, we propose a 2-stage low-light image enhancement method called
Self-Reference Deep Adaptive Curve Estimation (Self-DACE). In the first stage,
we present an intuitive, lightweight, fast, and unsupervised luminance
enhancement algorithm. The algorithm is based on a novel low-light enhancement
curve that can be used to locally boost image brightness. We also propose a new
loss function with a simplified physical model designed to preserve natural
images' color, structure, and fidelity. We use a vanilla CNN to map each pixel
through deep Adaptive Adjustment Curves (AAC) while preserving the local image
structure. Secondly, we introduce the corresponding denoising scheme to remove
the latent noise in the darkness. We approximately model the noise in the dark
and deploy a Denoising-Net to estimate and remove the noise after the first
stage. Exhaustive qualitative and quantitative analysis shows that our method
outperforms existing state-of-the-art algorithms on multiple real-world
datasets
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