Shadow Detection and Removal in Images using Matlab


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A tendency to detect the shadows and removal of its influence in profilometry is carried out. The planned algorithmic program was developed in MATLAB. Elimination of shadows features a crucial impact to the operates of unwrapping section. Unwrapping of section (one a part of the profilometry procedure) will fail once no shadow detection is employed. Among altered basal operations, morphological operations are activated in this algebraic program. Finally, the acquired after-effects are evaluated and mentioned.



Shadows and shadings in pictures could cause abominable issues on angel analysis. A shadow happens once associate object part or entirely occludes direct light-weight from a supply of illumination. In general, shadows are divided into 2 major classes: self and solid shadows. A self-shadow happens within the portion of the associated object that isn’t light by direct light-weight. A solid shadow is that the space projected by the article within the direction of direct light-weight. supported the intensity, the shadows are of 2 varieties − onerous and soft shadows. The bendable caliginosity absorbs the feel of the accomplishments surface, admitting the arduous caliginosity are too aphetic and accept actual little texture. so the apprehension of arduous caliginosity is difficult as they’ll be mistaken as aphetic altar instead of shadows. those’ a lot of the adumbration apprehension strategies would like assorted pictures for camera standardization, the simplest technique should be able to extract shadows from one image. This paper provides an easy technique to find and take away shadows from one RGB image. 


To find shadow into the RGB image is regenerate to associate workplace equivalent image. The workplace color area has 3 channels wherever L is that the Lightness channel, A and B are the 2 color channels. To find shadow 1st an RGB image has got to be regenerate to a workplace image. Then the beggarly ethics of the pixels in L, A and B planes of the angel care to be computed one by one. 


An addiction to are abusage morphological algebraic affairs to acquisition shadow is accepted. Some specific backdrop appears from the appliance of profilometry. As photos are added  with absolutely altered agreeable offered, specific algorithms are proposed .  a tendency to take into account the initial image of the scene (further referred to as Object), to boot the image with the projected pattern (further referred to as an Object_ pattern) and film with pattern projected to the background (further referred to as Pattern). Another vital truth is that we’ve depth map created by the stereo technique (further referred to as Depth_ stereo). It is prefered to improve this map by mistreatment bar graph effort. 


shadow detection and removal using matlab


  • Easy to detect
  • No loss edge detection
  • High accuracy
  • Low complexity 


The projected algorithms were enforced in MATLAB, so as to verify their operation. we have a tendency to created applicable functions and a computer program. The interface performs adumbration apprehension and approaching conception of abyss map abuse profilometry. Users have the likelihood to line up bound properties of morphological operations. The dead experiment well-tried usability of each projected algorithms for shadow detection. victimization the projected procedures, we will find shadows in pictures and so eliminate their influence. Consequently, the rule for unwrapping section works well and that we gain quality depth maps of objects that ought to guarantee the improvement of depth map obtained victimization stereo rule. 


[1] HU, Qingying; HARDING, Kevin G. Conversion from phase map to coordinate: Comparison among spatial carrier, Fourier transform, and phase shifting methods map to coordinate : Comparison among spatial carrier, Fourier transform, and phase shifting methods. Optics and Lasers in Engineering [online]. 2007, 47, 2, [cit. 2011-10-10]. Available on WWW: <>. ISSN 01438166.

[2] KREIS, Thomas. Handbook of Holographic Interferometry : Optical a digital methods. Weinheim : WILEY-VCH Verlag GmbH & Co. KGaA, 2005. 535 s. ISBN 3-527-40546-1.

[3] SU, Xianyu; CHEN, Wenjing. Fourier transform profilometry: : a review. Optics and Lasers in Engineering [online]. 2001, 35, 5, [cit. 2011-10-10]. Available on WWW: <>. ISSN 01438166.

[4] BIOUCAS-DIAS, J.M; VALADAO, G. Phase Unwrapping via Graph Cuts . IEEE Transactions on Image Processing [online]. 2007, 16, 3, [cit. 2011-9-25]. Available on WWW:< er=4099386>. ISSN 1057-7149.

[5] SCHARSTEIN, Daniel; SZELISKI, Richard. High-Accuracy Stereo Maps Using Structured Light. In Computer Vision and Pattern Recognition [online]. [s.l.] : [s.n.], 2003 [cit. 2011-10-10]. Available on WWW: <>. ISBN 0-7695-1900-8 , ISSN 1063-6919.

[6] Li Xu, Feihu Qi, Renjie Jiang, Yunfeng Hao, Guorong Wu,.: “Shadow detection and removal in real images: A survey” Technical report, Shanghai Jiao Tong University, 2006.

[7] HUANG, Chung-Hsien ; WU, Ruei-Cheng . An Online Learning Method for Shadow Detection . In 2010 Fourth Pacific-Rim Symposium on Image and Video Technology [online]. Singapore : Gwangju Institute of Science and Technology, 2010 [cit. 2011-09-25]. Available on WWW: <>. ISBN 978-1-4244-8890-2.

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