Robust All-in-focus Super-Resolution for Focal Stack Photography

Lee, Minhaeng; Tai, Yu-Wing

doi:10.1109/tip.2016.2523419

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…We compare USEQ superpixel interpolation with nearest-neighbor interpolation [ 40 ], bilinear interpolation [ 41 ], and bicubic interpolation [ 42 ] at different downsampling rates (i.e., 0.125, 0.25, and 0.5). Figure 9 shows the average PSNR evaluation results for each dataset.…”

Section: Resultsmentioning

confidence: 99%

Lung Field Segmentation in Chest X-ray Images Using Superpixel Resizing and Encoder–Decoder Segmentation Networks

Lee

Saidy

et al. 2022

Bioengineering

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Lung segmentation of chest X-ray (CXR) images is a fundamental step in many diagnostic applications. Most lung field segmentation methods reduce the image size to speed up the subsequent processing time. Then, the low-resolution result is upsampled to the original high-resolution image. Nevertheless, the image boundaries become blurred after the downsampling and upsampling steps. It is necessary to alleviate blurred boundaries during downsampling and upsampling. In this paper, we incorporate the lung field segmentation with the superpixel resizing framework to achieve the goal. The superpixel resizing framework upsamples the segmentation results based on the superpixel boundary information obtained from the downsampling process. Using this method, not only can the computation time of high-resolution medical image segmentation be reduced, but also the quality of the segmentation results can be preserved. We evaluate the proposed method on JSRT, LIDC-IDRI, and ANH datasets. The experimental results show that the proposed superpixel resizing framework outperforms other traditional image resizing methods. Furthermore, combining the segmentation network and the superpixel resizing framework, the proposed method achieves better results with an average time score of 4.6 s on CPU and 0.02 s on GPU.

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Section: Resultsmentioning

confidence: 99%

Lung Field Segmentation in Chest X-ray Images Using Superpixel Resizing and Encoder–Decoder Segmentation Networks

Lee

Saidy

et al. 2022

Bioengineering

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“…The light field in the image space can be regarded as the process of light propagation on different imaging planes, corresponding to the focal stack [42] , [43] , [44] . The focal stack is a sequence of images that are focused on different imaging planes.…”

Section: Related Workmentioning

confidence: 99%

Diffusion equation based parameterization of light field and computational imaging model

Liu

Qiu²

2022

Heliyon

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“…These methods are efficient but produce blurry results. Moreover, dedicated hardware-equipped devices, such as jittering [2] and focal stack [23] cameras, allow the use of multiple images to solve the LR image problem. However, these specialized devices incur additional costs and cannot be used to restore images captured with conventional cameras in the past.…”

Section: Mlsr (Ours)mentioning

confidence: 99%

Fast Adaptation to Super-Resolution Networks via Meta-Learning

Park¹,

Yoo²,

Cho³

et al. 2020

Preprint

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Conventional supervised super-resolution (SR) approaches are trained with massive external SR datasets but fail to exploit desirable properties of the given test image. On the other hand, self-supervised SR approaches utilize the internal information within a test image but suffer from computational complexity in run-time. In this work, we observe the opportunity for further improvement of the performance of SISR without changing the architecture of conventional SR networks by practically exploiting additional information given from the input image. In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time. Then, in the test stage, parameters of this meta-learned network are rapidly fine-tuned with only a few iterations by only using the given low-resolution image. The adaptation at the test time takes full advantage of patch-recurrence property observed in natural images. Our method effectively handles unknown SR kernels and can be applied to any existing model. We demonstrate that the proposed model-agnostic approach consistently improves the performance of conventional SR networks on various benchmark SR datasets.

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Robust All-in-focus Super-Resolution for Focal Stack Photography

Cited by 13 publications

References 29 publications

Lung Field Segmentation in Chest X-ray Images Using Superpixel Resizing and Encoder–Decoder Segmentation Networks

Lung Field Segmentation in Chest X-ray Images Using Superpixel Resizing and Encoder–Decoder Segmentation Networks

Diffusion equation based parameterization of light field and computational imaging model

Fast Adaptation to Super-Resolution Networks via Meta-Learning

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