SHARDS: Efficient SHAdow Removal using Dual Stage Network for High-Resolution Images

Sen, Mrinmoy; Chermala, Sai Pradyumna; Nagori, Nazrinbanu Nurmohammad; Peddigari, Venkat; Mathur, Praful; Prasad, B. V. S. S. S.; Jeong, Moon-Hwan

doi:10.1109/wacv56688.2023.00185

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The problem of shadow removal resulting in low resolution images and the usages of heavy network architecture which cannot run limited resources. SHARDS, which is a shadow removal method for high resolution images, solve these problems in two stages; a LSRNet and a DRNet [33] .…”

Section: Image Based Methodsmentioning

confidence: 99%

Design and analysis of shadow detection and removal scheme in real-time still images

Ali,

Karim,

Akhtar

et al. 2024

Comput. Soft. Media. Appl.

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This research’s main objective is to study and evaluate the detection and removal of undesired shadows from still images since these shadows might mask important information caused by light sources and other obstructions. A variety of methods for detecting and eliminating shadows as well as object tracking approaches based on movement estimation and identification are investigated. This includes shadow removal methods like background subtraction, which are intended to improve obstacle recognition of the source item and increase the accuracy of shadow removal from objects. When new items enter the frame, they are first distinguished from the background using a reference frame. The tracking procedure is made more difficult by the merging of the shadow with the foreground object. The approach highlights the difficulties in object detection owing to frequent occurrences of obstacles by using morphological procedures for shadow identification and removal. The proposed approach uses feature extraction is also discussed, highlighting its importance in image processing research and the use of suggested methods to get over obstacles in image sequences. The proposed method for shadow identification and removal offers a novel approach to improve image processing when dealing with still images. The purpose of this technique is to better detect and remove shadows from images, which will increase the precision of object tracking and detection. Depending on the type of images being processed, the process begins with initializing a background model, which is based on a static image background.

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Section: Image Based Methodsmentioning

confidence: 99%

Design and analysis of shadow detection and removal scheme in real-time still images

Ali,

Karim,

Akhtar

et al. 2024

Comput. Soft. Media. Appl.

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show abstract

“…There are many works on shadow removal and image restoration, such as semisupervised models with guidance [1,4,5,6,7,8,9,10,11,2,3], GAN's methods [12,13,14,15,16,17,18,19], and some unsupervised methods [20,21,22,23,24,25,26].…”

Section: Introductionmentioning

confidence: 99%

Shadclips:When Parameter-Efficient Fine-Tuning with Multimodal Meets Shadow Removal

Zhang,

Xu,

Tang

et al. 2024

Preprint

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Segment Anything Model (SAM), an advanced universal image segmentation model trained on an expansive visual dataset, has set a new benchmark in image segmentation and computer vision. However, it faced challenges when it came to distinguishing between shadows and their backgrounds. To address this, we proposed ShadClips, which consists of SAM-optimizer and SONet. It has dramatically enhanced SAM’s ability to segment shadow images, differentiating between the background and both soft and hard shadows adeptly. Due to its dependence on pixel point inputs, the SAM-Optimizer interface could do better. This method presents challenges, especially when dealing with long, extended shadows. To make the user experience more intuitive and effective, we incorporated the capabilities of CLIPs. Therefore, simple text descriptions like “A photo of a shadow” can be used to guide the SAM-Optimizer, allowing it to select the most relevant shadow mask from SAM’s comprehensive category list. Meanwhile, we introduce SONet to shadow removal. A large number of experiments on ISTD/SRD prove that the proposed method is effective and satisfactory. The source code of the ShadClips can be accessed from https://github.com/zhangbaijin/SAM-helps-Shadow.

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NTIRE 2023 Image Shadow Removal Challenge Report

Vasluianu¹,

Seizinger²,

Timofte³

et al. 2023

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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SHARDS: Efficient SHAdow Removal using Dual Stage Network for High-Resolution Images

Cited by 4 publications

References 23 publications

Design and analysis of shadow detection and removal scheme in real-time still images

Design and analysis of shadow detection and removal scheme in real-time still images

Shadclips:When Parameter-Efficient Fine-Tuning with Multimodal Meets Shadow Removal

NTIRE 2023 Image Shadow Removal Challenge Report

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