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P. S. Hrishikesh

Bio: P. S. Hrishikesh is an academic researcher from College of Engineering, Trivandrum. The author has contributed to research in topics: Image restoration & Digital imaging. The author has an hindex of 6, co-authored 9 publications receiving 144 citations.

Papers
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Proceedings ArticleDOI
14 Jun 2020
TL;DR: The NTIRE 2020 challenge on perceptual extreme super-resolution as mentioned in this paper focused on super-resolving an input image with a magnification factor ×16 based on a set of prior examples of low and corresponding high resolution images.
Abstract: This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor ×16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.

47 citations

Book ChapterDOI
23 Aug 2020
TL;DR: The AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results as discussed by the authors was held in 2019, where the goal was to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption.
Abstract: This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor \(\times \)4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

41 citations

Book ChapterDOI
23 Aug 2020
TL;DR: The AIM 2020 challenge on virtual image relighting and illumination estimation as discussed by the authors focused on one-to-one relighting, where the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation.
Abstract: We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i.e., light source position). The goal of the second track was to estimate illumination settings, namely the color temperature and orientation, from a given image. Lastly, the third track dealt with any-to-any relighting, thus a generalization of the first track. The target color temperature and orientation, rather than being pre-determined, are instead given by a guide image. Participants were allowed to make use of their track 1 and 2 solutions for track 3. The tracks had 94, 52, and 56 registered participants, respectively, leading to 20 confirmed submissions in the final competition stage.

39 citations

Book ChapterDOI
23 Aug 2020
TL;DR: The second AIM realistic bokeh effect rendering challenge as discussed by the authors was the first attempt to learn a realistic shallow focus technique using a large-scale EBB! dataset consisting of 5K shallow/wide depth-of-field image pairs captured using the Canon 7D DSLR camera.
Abstract: This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world bokeh simulation problem, where the goal was to learn a realistic shallow focus technique using a large-scale EBB! bokeh dataset consisting of 5K shallow/wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The participants had to render bokeh effect based on only one single frame without any additional data from other cameras or sensors. The target metric used in this challenge combined the runtime and the perceptual quality of the solutions measured in the user study. To ensure the efficiency of the submitted models, we measured their runtime on standard desktop CPUs as well as were running the models on smartphone GPUs. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical bokeh effect rendering problem.

33 citations

Book ChapterDOI
23 Aug 2020
TL;DR: The video extreme super-resolution challenge at ECCV 2020 as discussed by the authors was the first challenge to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos.
Abstract: This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020 Common scaling factors for learned video super-resolution (VSR) do not go beyond factor 4 Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details The task in this challenge is to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos A single pixel in the low-resolution (LR) domain corresponds to 256 pixels in the high-resolution (HR) domain Due to this massive information loss, it is hard to accurately restore the missing information Track 1 is set up to gauge the state-of-the-art for such a demanding task, where fidelity to the ground truth is measured by PSNR and SSIM Perceptually higher quality can be achieved in trade-off for fidelity by generating plausible high-frequency content Track 2 therefore aims at generating visually pleasing results, which are ranked according to human perception, evaluated by a user study In contrast to single image super-resolution (SISR), VSR can benefit from additional information in the temporal domain However, this also imposes an additional requirement, as the generated frames need to be consistent along time

20 citations


Cited by
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Posted Content
Hengyuan Zhao1, Xiangtao Kong1, Jingwen He1, Yu Qiao1, Chao Dong1 
TL;DR: This work designs a lightweight convolutional neural network for image super resolution with a newly proposed pixel attention scheme that could achieve similar performance as the lightweight networks - SRResNet and CARN, but with only 272K parameters.
Abstract: This work aims at designing a lightweight convolutional neural network for image super resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective network with a newly proposed pixel attention scheme. Pixel attention (PA) is similar as channel attention and spatial attention in formulation. The difference is that PA produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results. On the basis of PA, we propose two building blocks for the main branch and the reconstruction branch, respectively. The first one - SC-PA block has the same structure as the Self-Calibrated convolution but with our PA layer. This block is much more efficient than conventional residual/dense blocks, for its twobranch architecture and attention scheme. While the second one - UPA block combines the nearest-neighbor upsampling, convolution and PA layers. It improves the final reconstruction quality with little parameter cost. Our final model- PAN could achieve similar performance as the lightweight networks - SRResNet and CARN, but with only 272K parameters (17.92% of SRResNet and 17.09% of CARN). The effectiveness of each proposed component is also validated by ablation study. The code is available at this https URL.

128 citations

Posted Content
TL;DR: The feature distillation connection (FDC) is proposed that is functionally equivalent to the channel splitting operation while being more lightweight and flexible and can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distilling network (RFDN).
Abstract: Recent advances in single image super-resolution (SISR) explored the power of convolutional neural network (CNN) to achieve a better performance. Despite the great success of CNN-based methods, it is not easy to apply these methods to edge devices due to the requirement of heavy computation. To solve this problem, various fast and lightweight CNN models have been proposed. The information distillation network is one of the state-of-the-art methods, which adopts the channel splitting operation to extract distilled features. However, it is not clear enough how this operation helps in the design of efficient SISR models. In this paper, we propose the feature distillation connection (FDC) that is functionally equivalent to the channel splitting operation while being more lightweight and flexible. Thanks to FDC, we can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distillation network (RFDN). RFDN uses multiple feature distillation connections to learn more discriminative feature representations. We also propose a shallow residual block (SRB) as the main building block of RFDN so that the network can benefit most from residual learning while still being lightweight enough. Extensive experimental results show that the proposed RFDN achieve a better trade-off against the state-of-the-art methods in terms of performance and model complexity. Moreover, we propose an enhanced RFDN (E-RFDN) and won the first place in the AIM 2020 efficient super-resolution challenge. Code will be available at this https URL.

125 citations

Proceedings ArticleDOI
16 Jun 2019
TL;DR: The 3rd NTIRE challenge on single-image super-resolution (restoration of rich details in a low-resolution image) is reviewed with a focus on proposed solutions and results and the state-of-the-art in real-world single image super- resolution.
Abstract: This paper reviewed the 3rd NTIRE challenge on single-image super-resolution (restoration of rich details in a low-resolution image) with a focus on proposed solutions and results. The challenge had 1 track, which was aimed at the real-world single image super-resolution problem with an unknown scaling factor. Participants were mapping low-resolution images captured by a DSLR camera with a shorter focal length to their high-resolution images captured at a longer focal length. With this challenge, we introduced a novel real-world super-resolution dataset (RealSR). The track had 403 registered participants, and 36 teams competed in the final testing phase. They gauge the state-of-the-art in real-world single image super-resolution.

118 citations

Posted Content
TL;DR: The NTIRE 2020 challenge addresses the real world setting, where paired true high and low-resolution images are unavailable, and the ultimate goal is to achieve the best perceptual quality, evaluated using a human study.
Abstract: This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image Processing artifacts, the aim is to super-resolve images with synthetically generated image processing artifacts. This allows for quantitative benchmarking of the approaches \wrt a ground-truth image. In Track 2: Smartphone Images, real low-quality smart phone images have to be super-resolved. In both tracks, the ultimate goal is to achieve the best perceptual quality, evaluated using a human study. This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019. In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.

92 citations

Book ChapterDOI
Hengyuan Zhao1, Xiangtao Kong1, Jingwen He1, Yu Qiao1, Chao Dong1 
23 Aug 2020
TL;DR: Zhao et al. as discussed by the authors designed a lightweight convolutional neural network with a pixel attention scheme, which produces 3D attention maps instead of a 1D attention vector or a 2D map.
Abstract: This work aims at designing a lightweight convolutional neural network for image super resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective network with a newly proposed pixel attention scheme. Pixel attention (PA) is similar as channel attention and spatial attention in formulation. The difference is that PA produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results. On the basis of PA, we propose two building blocks for the main branch and the reconstruction branch, respectively. The first one—SC-PA block has the same structure as the Self-Calibrated convolution but with our PA layer. This block is much more efficient than conventional residual/dense blocks, for its two-branch architecture and attention scheme. While the second one—U-PA block combines the nearest-neighbor upsampling, convolution and PA layers. It improves the final reconstruction quality with little parameter cost. Our final model—PAN could achieve similar performance as the lightweight networks—SRResNet and CARN, but with only 272K parameters (17.92% of SRResNet and 17.09% of CARN). The effectiveness of each proposed component is also validated by ablation study. The code is available at https://github.com/zhaohengyuan1/PAN.

80 citations