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Shadow removal is an important problem in computer vision, since the presence of shadows complicates core computer vision tasks, including image segmentation and object recognition. Most state-of-the-art shadow removal methods are based on complex deep learning architectures, which require training on a large amount of data. In this paper a novel a...
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... Most of the current datasets developed for benchmarking shadow removal from images contain scenes that are very simple, e.g., including one or a couple of objects, or limited background variation. This can limit the capacity of supervised DL-based shadow removal Simple Unsupervised Shadow removal (SUShe), was proposed [14]. That method combines a physics-based optimization algorithm with color feature extraction for shadow detection, and it recovers the luminosity of the shadowed areas by leveraging superpixel segmentation and histogram matching. ...
... Experimental Setup. The comparative evaluation of the proposed SHAU architecture involved nine state-of-the-art shadow removal methods that cover a diverse range of approaches that rely on different types of networks to remove shadows, including ST-CGAN [17], DC-ShadowNet [31], LG-ShadowNet [58], SP+M+I-Net [19], Fu et al. [59], CNSNet [23], SG-ShadowNet [22], ShadowFormer [24] and a physics- [14], which does not require training. For a fair comparison, the results are reproduced utilizing the official source code and hyperparameters of each reported method, with the exception of CNSNet for which the source code was not available. ...
Effective shadow removal is pivotal in enhancing the visual quality of images in various applications, ranging from computer vision to digital photography. During the last decades physics and machine learning -based methodologies have been proposed; however, most of them have limited capacity in capturing complex shadow patterns due to restrictive model assumptions, neglecting the fact that shadows usually appear at different scales. Also, current datasets used for benchmarking shadow removal are composed of a limited number of images with simple scenes containing mainly uniform shadows cast by single objects, whereas only a few of them include both manual shadow annotations and paired shadow-free images. Aiming to address all these limitations in the context of natural scene imaging, including urban environments with complex scenes, the contribution of this study is twofold: a) it proposes a novel deep learning architecture, named Soft-Hard Attention U-net (SHAU), focusing on multiscale shadow removal; b) it provides a novel synthetic dataset, named Multiscale Shadow Removal Dataset (MSRD), containing complex shadow patterns of multiple scales, aiming to serve as a privacy-preserving dataset for a more comprehensive benchmarking of future shadow removal methodologies. Key architectural components of SHAU are the soft and hard attention modules, which along with multiscale feature extraction blocks enable effective shadow removal of different scales and intensities. The results demonstrate the effectiveness of SHAU over the relevant state-of-the-art shadow removal methods across various benchmark datasets, improving the Peak Signal-to-Noise Ratio and Root Mean Square Error for the shadow area by 25.1% and 61.3%, respectively.
... Still image subdivision, which needs to be, fragmented the actual object from the groundwork to inspect the image appropriately and recognize the contents of the images very carefully through a sequence of steps can be seen in the Figure 2. flowchart. In this unique circumstance, the edge detection is a key contraption for image processing [14,15] . Flowchart of the proposed shadow removal method [5] . ...
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.
