Fully End-to-End Learning Based Conditional Boundary Equilibrium GAN with Receptive Field Sizes Enlarged for Single Ultra-High Resolution Image Dehazing

Ki, Sehwan; Sim, Hyeonjun; Choi, Jae-Seok; Seo, Soomin; Kim, Saehun; Kim, Munchurl

doi:10.1109/cvprw.2018.00126

Cited by 26 publications

(4 citation statements)

References 11 publications

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“…The neural models employed are CNNs, typically trained on images with artificially added blur or haze, using a MSE loss function [19,16,17,7,3]. Recently, datasets with both hazy and haze-free images were introduced [1] and solutions such as the one of Ki et al [13] were proposed, which use a GAN, in addition to L1 and perceptual losses. Similar techniques are effective for image denoising as well [27,25,24,21].…”

Section: Related Workmentioning

confidence: 99%

Fast Perceptual Image Enhancement

Stoutz¹,

Ignatov²,

Kobyshev³

et al. 2018

Preprint

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The vast majority of photos taken today are by mobile phones. While their quality is rapidly growing, due to physical limitations and cost constraints, mobile phone cameras struggle to compare in quality with DSLR cameras. This motivates us to computationally enhance these images. We extend upon the results of Ignatov et al., where they are able to translate images from compact mobile cameras into images with comparable quality to high-resolution photos taken by DSLR cameras. However, the neural models employed require large amounts of computational resources and are not lightweight enough to run on mobile devices. We build upon the prior work and explore different network architectures targeting an increase in image quality and speed. With an efficient network architecture which does most of its processing in a lower spatial resolution, we achieve a significantly higher mean opinion score (MOS) than the baseline while speeding up the computation by 6.3× on a consumer-grade CPU. This suggests a promising direction for neural-network-based photo enhancement using the phone hardware of the future.

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Section: Related Workmentioning

confidence: 99%

Fast Perceptual Image Enhancement

Stoutz¹,

Ignatov²,

Kobyshev³

et al. 2018

Preprint

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“…Porav et al [13] created a setup to capture simultaneous scenes with and without rain effects by dripping water on one camera lens while keeping the second one clean. Image restoration approaches for Dehazing was proposed in [14], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric

Das¹,

Křížek²,

Sistu³

et al. 2020

Preprint

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Automotive cameras, particularly surround-view cameras, tend to get soiled by mud, water, snow, etc. For higher levels of autonomous driving, it is necessary to have a soiling detection algorithm which will trigger an automatic cleaning system. Localized detection of soiling in an image is necessary to control the cleaning system. It is also necessary to enable partial functionality in unsoiled areas while reducing confidence in soiled areas. Although this can be solved using a semantic segmentation task, we explore a more efficient solution targeting deployment in low power embedded system. We propose a novel method to regress the area of each soiling type within a tile directly. We refer to this as coverage. The proposed approach is better than learning the dominant class in a tile as multiple soiling types occur within a tile commonly. It also has the advantage of dealing with coarse polygon annotation, which will cause the segmentation task. The proposed soiling coverage decoder is an order of magnitude faster than an equivalent segmentation decoder. We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. A portion of the dataset used will be released publicly as part of our WoodScape dataset [1] to encourage further research.

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“…Another successful effort has been to dehaze [2] the high resolution ultrasound images. For both the approaches, the network was trained with a pair of defective-clean images.…”

Section: Introductionmentioning

confidence: 99%

SoildNet: Soiling Degradation Detection in Autonomous Driving

Das

2019

Preprint

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In the field of autonomous driving, camera sensors are extremely prone to soiling because they are located outside of the car and interact with environmental sources of soiling such as rain drops, snow, dust, sand, mud and so on. This can lead to either partial or complete vision degradation. Hence detecting such decay in vision is very important for safety and overall to preserve the functionality of the "autonomous" components in autonomous driving. The contribution of this work involves: 1) Designing a Deep Convolutional Neural Network (DCNN) based baseline network, 2) Exploiting several network remodelling techniques such as employing static and dynamic group convolution, channel reordering to compress the baseline architecture and make it suitable for low power embedded systems with ∼1 TOPS, 3) Comparing various result metrics of all interim networks dedicated for soiling degradation detection at tile level of size 64 × 64 on input resolution 1280 × 768. The compressed network, is called SoildNet (Sand, snOw, raIn/dIrt, oiL, Dust/muD) that uses only 9.72% trainable parameters of the base network and reduces the model size by more than 7 times with no loss in accuracy.

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Fully End-to-End Learning Based Conditional Boundary Equilibrium GAN with Receptive Field Sizes Enlarged for Single Ultra-High Resolution Image Dehazing

Cited by 26 publications

References 11 publications

Fast Perceptual Image Enhancement

Fast Perceptual Image Enhancement

TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric

SoildNet: Soiling Degradation Detection in Autonomous Driving

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