TY - JOUR
T1 - Image denoising with morphology- and size-adaptive block-matching transform domain filtering
AU - Hou, Yingkun
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported by the National Science Foundation of China [grant numbers 61379015, 61463008]; the Natural Science Foundation of Shandong Province [grant number ZR2011FM004]; the Science and Technology Development Project of Taian City [grant number 20113062]; and the Talent Introduction Project of the Taishan University.
Publisher Copyright:
© 2018, The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - BM3D is a state-of-the-art image denoising method. Its denoised results in the regions with strong edges can often be better than in the regions with smooth or weak edges, due to more accurate block-matching for the strong-edge regions. So using adaptive block sizes on different image regions may result in better image denoising. Based on these observations, in this paper, we first partition each image into regions belonging to one of the three morphological components, i.e., contour, texture, and smooth components, according to the regional energy of alternating current (AC) coefficients of discrete cosine transform (DCT). Then, we can adaptively determine the block size for each morphological component. Specifically, we use the smallest block size for the contour components, the medium block size for the texture components, and the largest block size for the smooth components. To better preserve image details, we also use a multi-stage strategy to implement image denoising, where every stage is similar to the BM3D method, except using adaptive sizes and different transform dimensions. Experimental results show that our proposed algorithm can achieve higher PSNR and MSSIM values than the BM3D method, and also better visual quality of denoised images than by the BM3D method and some other existing state-of-the-art methods.
AB - BM3D is a state-of-the-art image denoising method. Its denoised results in the regions with strong edges can often be better than in the regions with smooth or weak edges, due to more accurate block-matching for the strong-edge regions. So using adaptive block sizes on different image regions may result in better image denoising. Based on these observations, in this paper, we first partition each image into regions belonging to one of the three morphological components, i.e., contour, texture, and smooth components, according to the regional energy of alternating current (AC) coefficients of discrete cosine transform (DCT). Then, we can adaptively determine the block size for each morphological component. Specifically, we use the smallest block size for the contour components, the medium block size for the texture components, and the largest block size for the smooth components. To better preserve image details, we also use a multi-stage strategy to implement image denoising, where every stage is similar to the BM3D method, except using adaptive sizes and different transform dimensions. Experimental results show that our proposed algorithm can achieve higher PSNR and MSSIM values than the BM3D method, and also better visual quality of denoised images than by the BM3D method and some other existing state-of-the-art methods.
KW - Block-matching
KW - Image denoising
KW - Morphological component
KW - Size-adaptive filtering
UR - http://www.scopus.com/inward/record.url?scp=85050373430&partnerID=8YFLogxK
U2 - 10.1186/s13640-018-0301-y
DO - 10.1186/s13640-018-0301-y
M3 - Article
AN - SCOPUS:85050373430
SN - 1687-5176
VL - 2018
JO - Eurasip Journal on Image and Video Processing
JF - Eurasip Journal on Image and Video Processing
IS - 1
M1 - 59
ER -