114 research outputs found
Fast single image defogging with robust sky detection
Haze is a source of unreliability for computer vision applications in outdoor scenarios, and it is usually caused by atmospheric conditions. The Dark Channel Prior (DCP) has shown remarkable results in image defogging with three main limitations: 1) high time-consumption, 2) artifact generation, and 3) sky-region over-saturation. Therefore, current work has focused on improving processing time without losing restoration quality and avoiding image artifacts during image defogging. Hence in this research, a novel methodology based on depth approximations through DCP, local Shannon entropy, and Fast Guided Filter is proposed for reducing artifacts and improving image recovery on sky regions with low computation time. The proposed-method performance is assessed using more than 500 images from three datasets: Hybrid Subjective Testing Set from Realistic Single Image Dehazing (HSTS-RESIDE), the Synthetic Objective Testing Set from RESIDE (SOTS-RESIDE) and the HazeRD. Experimental results demonstrate that the proposed approach has an outstanding performance over state-of-the-art methods in reviewed literature, which is validated qualitatively and quantitatively through Peak Signal-to-Noise Ratio (PSNR), Naturalness Image Quality Evaluator (NIQE) and Structural SIMilarity (SSIM) index on retrieved images, considering different visual ranges, under distinct illumination and contrast conditions. Analyzing images with various resolutions, the method proposed in this work shows the lowest processing time under similar software and hardware conditions.This work was supported in part by the Centro en Investigaciones en Óptica (CIO) and the Consejo Nacional de Ciencia y Tecnología (CONACYT), and in part by the Barcelona Supercomputing Center.Peer ReviewedPostprint (published version
Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization
Recently, deep learning-based methods have dominated image dehazing domain.
Although very competitive dehazing performance has been achieved with
sophisticated models, effective solutions for extracting useful features are
still under-explored. In addition, non-local network, which has made a
breakthrough in many vision tasks, has not been appropriately applied to image
dehazing. Thus, a multi-receptive-field non-local network (MRFNLN) consisting
of the multi-stream feature attention block (MSFAB) and cross non-local block
(CNLB) is presented in this paper. We start with extracting richer features for
dehazing. Specifically, we design a multi-stream feature extraction (MSFE)
sub-block, which contains three parallel convolutions with different receptive
fields (i.e., , , ) for extracting multi-scale
features. Following MSFE, we employ an attention sub-block to make the model
adaptively focus on important channels/regions. The MSFE and attention
sub-blocks constitute our MSFAB. Then, we design a cross non-local block
(CNLB), which can capture long-range dependencies beyond the query. Instead of
the same input source of query branch, the key and value branches are enhanced
by fusing more preceding features. CNLB is computation-friendly by leveraging a
spatial pyramid down-sampling (SPDS) strategy to reduce the computation and
memory consumption without sacrificing the performance. Last but not least, a
novel detail-focused contrastive regularization (DFCR) is presented by
emphasizing the low-level details and ignoring the high-level semantic
information in the representation space. Comprehensive experimental results
demonstrate that the proposed MRFNLN model outperforms recent state-of-the-art
dehazing methods with less than 1.5 Million parameters.Comment: submitted to IEEE TCYB for possible publicatio
ED-Dehaze Net: Encoder and Decoder Dehaze Network
The presence of haze will significantly reduce the quality of images, such as resulting in lower contrast and blurry details. This paper proposes a novel end-to-end dehazing method, called Encoder and Decoder Dehaze Network (ED-Dehaze Net), which contains a Generator and a Discriminator. In particular, the Generator uses an Encoder-Decoder structure to effectively extract the texture and semantic features of hazy images. Between the Encoder and Decoder we use Multi-Scale Convolution Block (MSCB) to enhance the process of feature extraction. The proposed ED-Dehaze Net is trained by combining Adversarial Loss, Perceptual Loss and Smooth L1 Loss. Quantitative and qualitative experimental results showed that our method can obtain the state-of-the-art dehazing performance
Dehazing Ultrasound using Diffusion Models
Echocardiography has been a prominent tool for the diagnosis of cardiac
disease. However, these diagnoses can be heavily impeded by poor image quality.
Acoustic clutter emerges due to multipath reflections imposed by layers of
skin, subcutaneous fat, and intercostal muscle between the transducer and
heart. As a result, haze and other noise artifacts pose a real challenge to
cardiac ultrasound imaging. In many cases, especially with difficult-to-image
patients such as patients with obesity, a diagnosis from B-Mode ultrasound
imaging is effectively rendered unusable, forcing sonographers to resort to
contrast-enhanced ultrasound examinations or refer patients to other imaging
modalities. Tissue harmonic imaging has been a popular approach to combat haze,
but in severe cases is still heavily impacted by haze. Alternatively, denoising
algorithms are typically unable to remove highly structured and correlated
noise, such as haze. It remains a challenge to accurately describe the
statistical properties of structured haze, and develop an inference method to
subsequently remove it. Diffusion models have emerged as powerful generative
models and have shown their effectiveness in a variety of inverse problems. In
this work, we present a joint posterior sampling framework that combines two
separate diffusion models to model the distribution of both clean ultrasound
and haze in an unsupervised manner. Furthermore, we demonstrate techniques for
effectively training diffusion models on radio-frequency ultrasound data and
highlight the advantages over image data. Experiments on both \emph{in-vitro}
and \emph{in-vivo} cardiac datasets show that the proposed dehazing method
effectively removes haze while preserving signals from weakly reflected tissue.Comment: 10 pages, 11 figures, preprint IEEE submissio
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