Wavelet Deep Neural Network for Stripe Noise Removal

Guan, Juntao; Lai, Rui; Xiong, Ai

doi:10.1109/access.2019.2908720

Cited by 100 publications

(49 citation statements)

References 39 publications

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“…The other category is data-driven methods such as deep learning ones. The deep learning has been widely applied to address image processing problems including single image haze removal [19], [36], [28], single image rain removal [35], [38], single image denoising [21], [32], as well as single image brightening. Li et al [27] designed a LightNet to predict the mapping relations between the weakly illuminated image and the corresponding illumination map.…”

Section: Relevant Work On Single Image Brighteningmentioning

confidence: 99%

Single Image Brightening via Multi-Scale Exposure Fusion With Hybrid Learning

Zheng

Yang

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

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A small ISO and a small exposure time are usually used to capture an image in back-or low-light condition which results in an image with negligible motion blur and small noise but looks dark. In this paper, a single image brightening algorithm is introduced to brighten such an image. The proposed algorithm includes a unique hybrid learning framework to generate two virtual images with large exposure times. The virtual images are first generated via intensity mapping functions (IMFs) which are computed using camera response functions (CRFs) and this is a model-driven approach. Both the virtual images are then enhanced by using a data-driven approach, i.e. a residual convolutional neural network to approach the ground truth images. The model-driven approach and the data-driven one compensate each other in the proposed hybrid learning framework. The final brightened image is obtained by fusing the original image and two virtual images via a multi-scale exposure fusion algorithm with properly defined weights. Experimental results show that the proposed brightening algorithm outperforms existing algorithms in terms of MEF-SSIM metric.

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Section: Relevant Work On Single Image Brighteningmentioning

confidence: 99%

Single Image Brightening via Multi-Scale Exposure Fusion With Hybrid Learning

Zheng

Yang

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

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“…Although the quality of IR images is improved, there is also much noise that needs to be reduced via images priors, such as smoothness and self-similarity. So the appropriate method for IR image denoising needs to be explored in the IR image super resolution procession without priors, such as [30]. Subsequently, the curvature filter [31] and guided filter methods [15,32], which can obtain well edge-preserving smoothing and less noise infrared images, are adopted for denoising.…”

Section: Ir Image Denoisingmentioning

confidence: 99%

RGB-IR Cross Input and Sub-Pixel Upsampling Network for Infrared Image Super-Resolution

Zhou

Qian

et al. 2020

Sensors

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Deep learning-based image super-resolution has shown significantly good performance in improving image quality. In this paper, the RGB-IR cross input and sub-pixel upsampling network is proposed to increase the spatial resolution of an Infrared (IR) image by combining it with a color image of higher spatial resolution obtained with a different imaging modality. Specifically, this is accomplished by fusion of the features map of two RGB-IR inputs in the reconstruction of an infrared image. To improve the accuracy of feature extraction, deconvolution is replaced by sub-pixel convolution to upsample image in the network. Then, the guided filter layer is introduced for image denoising of IR images, and it can preserve the image detail. In addition, the experimental dataset, which is collected by us, contains large numbers of RGB images and corresponding IR images with the same scene. Experimental results on our dataset and other datasets demonstrate that the method is superior to existing methods in accuracy and visual improvement.

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“…Hence, it is important to develop algorithms that can automatically remove these artifacts. To address the problem of stripe nonuniformity correction (NUC), various methods [2][3][4][5][6][7][8][9] have been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, more adaptive and efficient methods that can deal with nonuniformity are needed. One solution to this problem is utilizing a scene-based NUC framework [4][5][6][7]. Scene-based NUC methods estimate the calibration parameters from the scene.…”

Section: Introductionmentioning

confidence: 99%

Temporal-Spatial Nonlinear Filtering for Infrared Focal Plane Array Stripe Nonuniformity Correction

Qin

Zeng

et al. 2019

Symmetry

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In this work, we introduce a temporal-spatial approach for infrared focal plane array (IRFPA) stripe nonuniformity correction in infrared images that generates visually appealing results. We posit that the nonuniformity appears as a striped structure in the spatial domain and that the pixel values change slowly in the temporal domain. Based on this, we formulate our correction method in two steps. In the first step, weighted guided image filtering with our adaptive weight is utilized to predict the stripe nonuniformity using a single frame. In the second step, the temporal profile of each pixel can be formed using a few frames of successive nonuniformity images. Further, we present a temporal nonlinear diffusion equation to remove scene residuals from the temporal profile of nonuniformity images in order to estimate a more accurate value of the stripe nonuniformity. The results of extensive experiments demonstrate that the proposed nonuniformity correction algorithm substantially outperforms many state-of-the-art approaches, including both traditional and deep convolution-neural-network-based methods, on four popular infrared videos. In addition, the proposed method only requires a fraction (less than ten) of the video frames.

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Wavelet Deep Neural Network for Stripe Noise Removal

Cited by 100 publications

References 39 publications

Single Image Brightening via Multi-Scale Exposure Fusion With Hybrid Learning

Single Image Brightening via Multi-Scale Exposure Fusion With Hybrid Learning

RGB-IR Cross Input and Sub-Pixel Upsampling Network for Infrared Image Super-Resolution

Temporal-Spatial Nonlinear Filtering for Infrared Focal Plane Array Stripe Nonuniformity Correction

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